scholarly journals AIR POLLUTION PREDICTION MODELS DUE TO TRAFFIC VOLUME AND GREEN OPEN SPACE AVAILABILITY

2020 ◽  
Vol 5 (1) ◽  
pp. 41
Author(s):  
Bambang Endro Yuwono ◽  
Mayang Sari
Keyword(s):  
Air Pollution ◽  
Regression Analysis ◽  
Open Space ◽  
Prediction Models ◽  
Traffic Volume ◽  
Passenger Car ◽  
The Relationship ◽  
The City ◽  
Air Pollution Prediction ◽  
Pollution Prediction

ABSTRACT As the population grows, the development of the city increases, as a result the movement of transportation also increases. The development of the city can also affect a decrease in green open space. Increased vehicle traffic affects the increase in air pollution. Hence, there is very little research that mathematically connects the influence of traffic volume (passenger car units) and green open space with the level of air pollution. Green open space and the level of air pollution are directly measured on the field. Subsequently, the measurement is calculated by using the regression analysis to obtain a model of the relationship between green open space and traffic volume with the level of air pollution. The research was directly conducted at 3 locations, 2 locations in Jakarta (Semanggi and Tanah Kusir) and 1 location in South Tangerang. This model can be applied to predict that air pollution will occur as a result of traffic volume and the availability of green open space. Keyword: Air pollution, prediction, green open space, traffic volume

scholarly journals Computation of geographic variables for air pollution prediction models in South Korea

2015 ◽  
Vol 30 ◽  
pp. e2015010 ◽  
Author(s):  
Youngseob Eum ◽  
Insang Song ◽  
Hwan-Cheol Kim ◽  
Jong-Han Leem ◽  
Sun-Young Kim
Keyword(s):  
Air Pollution ◽  
South Korea ◽  
Prediction Models ◽  
Air Pollution Prediction ◽  
Pollution Prediction

scholarly journals Air pollution prediction models of particles, As, Cd, Ni and Pb in a highly industrialized area in Castellón (NE, Spain)

2011 ◽  
Vol 66 (3) ◽  
pp. 879-888 ◽  
Author(s):  
A. B. Vicente ◽  
M. M. Jordán ◽  
T. Sanfeliu ◽  
A. Sánchez ◽  
Ma D. Esteban
Keyword(s):  
Air Pollution ◽  
Prediction Models ◽  
Air Pollution Prediction ◽  
Ne Spain ◽  
Pollution Prediction

Sensor-Based Air Pollution Prediction Using Deep CNN-LSTM

2020 ◽  
Author(s):  
Kabir Nagrecha ◽  
Pratyush Muthukumar ◽  
Emmanuel Cocom ◽  
Jeanne Holm ◽  
Dawn Comer ◽  
...  
Keyword(s):  
Air Pollution ◽  
Deep Cnn ◽  
Air Pollution Prediction ◽  
Pollution Prediction

scholarly journals Enhancing CFD-LES air pollution prediction accuracy using data assimilation

2019 ◽  
Vol 165 ◽  
pp. 106383 ◽  
Author(s):  
Elsa Aristodemou ◽  
Rossella Arcucci ◽  
Laetitia Mottet ◽  
Alan Robins ◽  
Christopher Pain ◽  
...  
Keyword(s):  
Air Pollution ◽  
Data Assimilation ◽  
Prediction Accuracy ◽  
Using Data ◽  
Air Pollution Prediction ◽  
Pollution Prediction

scholarly journals Socioeconomic inequalities and exposure to traffic-related air pollution in the city of São Paulo, Brazil

2014 ◽  
Vol 30 (1) ◽  
pp. 119-125 ◽  
Author(s):  
Mateus Habermann ◽  
Míriam Souza ◽  
Rogério Prado ◽  
Nelson Gouveia
Keyword(s):  
Air Pollution ◽  
Socioeconomic Status ◽  
Air Pollutants ◽  
Epidemiological Studies ◽  
Sao Paulo ◽  
Health Concern ◽  
Traffic Density ◽  
São Paulo ◽  
The Relationship ◽  
The City

Air pollution is a leading public health concern. In addition, poor populations have been reported as showing increased exposure to such pollution. The current study thus aimed to evaluate the socioeconomic status of the population exposed to vehicle-related air pollution in the city of São Paulo, Brazil. The study used data from the 2010 Census on head-of-household’s mean monthly income and the percentage of households connected to the sewage system. Exposure to air pollutants was estimated according to traffic density in the census tract plus a 200m surrounding buffer. The relationship between exposure and socioeconomic variables was analyzed by the Kruskal-Wallis test. Exposure increased with increasing socioeconomic status (p < 0.001). The population with the highest socioeconomic status lives in the most polluted areas of the city. However, place of residence alone is not capable of measuring exposure. The study suggests that future epidemiological studies include other indicators of vulnerability.

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