Modelling of Air Pollution Caused by Traffic Flows in Manado City, Indonesia

2019 ◽  
Vol 1 (1) ◽  
pp. 84-95
Author(s):  
Theo Kurniawan Sendow
Keyword(s):  
Air Pollution ◽  
Wind Speed ◽  
Primary Data ◽  
Vehicle Speed ◽  
Air Pollution Control ◽  
Traffic Flows ◽  
Building Density ◽  
Independent Variables ◽  
Co Concentration ◽  
High Building

This study was aimed to determine the level of CO concentration due to traffic flows, know the traffic (vehicle) volume, traffic (vehicle) speed and wind speed, find out the relationship between traffic (vehicle) volume, traffic (vehicle) speed, wind speed and CO concentration using a regression model as well as examine the parameters influencing air pollution due to traffic flows. The primary data used in this study were the sample data of CO tested directly in the field and the sampling was done using Ecoline 6000 Gas Analyzer tool. The research sites covered 1) the segment of Sam Ratulangi Street in Manado representing the street locations with many multi-rise buildings and high building density, 2) the segment of Ahmad Yani Street in Manado representing the street locations with many trees, and 3) the segment of Pierre Tendean Street in Manado representing the street locations with open areas (beachsides). In this modeling, the independent variables were the total traffic volume, the average traffic speed as well as the wind speed and direction. The dependent variable was Carbon Monoxide (CO) with increased concentrations. Using the three independent variables, there were total 7 (seven) variable combinations used. Then, the obtained model was validated using the surveyed data. The maximum vehicle volume was 4,281.60 pcu/hour (pcu = passenger car unit) and the maximum vehicle speed was 32 km/hour. Meanwhile, the maximum wind speed generated was 7.5 km/hour and the maximum level of air pollution (CO) was 12.86 ppm (ppm = part per million). In this study, it was obtained the best model for each of the three locations. The results showed that the air pollution (CO) level of street locations with low wind speed, such as Sam Ratulangi street which is a closed area with many multi-rise buildings and high building density, was much higher than that of street locations with many trees growing in the median of streets with a distance of 1 meter from the edge of street pavement and also higher than that of street locations with open areas (beachsides). This is because a higher wind speed can disseminate or divide the concentration level of air pollution (CO) to various places. Air pollution control covers three stages namely the prevention, countermeasure, and recovery of air quality.

scholarly journals Mechanism of Spatiotemporal Air Quality Response to Meteorological Parameters: A National-Scale Analysis in China

2019 ◽  
Vol 11 (14) ◽  
pp. 3957 ◽  
Author(s):  
Zhi Qiao ◽  
Feng Wu ◽  
Xinliang Xu ◽  
Jin Yang ◽  
Luo Liu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Wind Speed ◽  
Relative Humidity ◽  
Meteorological Parameters ◽  
Pollution Index ◽  
Hierarchical Cluster ◽  
Meteorological Conditions ◽  
Air Pollution Control ◽  
National Scale

The air quality over China exhibits seasonal and regional variation, resulting from heterogeneity in industrialization, and is highly affected by variability in meteorological conditions. We performed the first national-scale exploration of the relationship between the Air Pollution Index (API) and multiple meteorological parameters in China, using partial correlation and hierarchical cluster analyses. Relative humidity, wind speed, and temperature were the dominant factors influencing air quality year-round, due to their significant effects on pollutant dispersion and/or transformation of pollutants. The response of the API to single or multiple meteorological factors varied among cities and seasons, and a regional clustering of response mechanisms was observed, particularly in winter. Clear north–south differentiation was detected in the mechanisms of API response to relative humidity and wind speed. These findings provide insight into the spatiotemporal variation in air quality sensitivity to meteorological conditions, which will be useful for implementing regional air pollution control strategies.

ANALISIS PENGUKURAN KONSENTRASI KARBONMONOKSIDA (CO) PADA BREATHING ZONE PETUGAS PARKIR BASEMENT MALL KOTA BANDUNG

2019 ◽  
Author(s):  
Gede H Cahyana
Keyword(s):  
Air Pollution ◽  
Indoor Air ◽  
Indoor Air Pollution ◽  
Sampling Method ◽  
Breathing Zone ◽  
Gas Concentration ◽  
Average Value ◽  
Co Concentration ◽  
Personal Dosimeter ◽  
Co Gas

Indoor air pollution in closed room is one of the air pollution that gives serious threats to human health. One of them come from vehicle gas emissions in closed parking area. This research identifies and analyses CO concentration measured in Mall X parking man’s breathing zone with closed parking area and in Mall Y semi-opened parking area. CO measurement carried out by passive sampling method using Personal Dosimeter Tubes. Measurement result of CO gas concentration to parking man’s breathing zone in Mall X was 25 – 81,25 ppm with average value in 50 ± 26,15 ppm. Meanwhile CO gas concentration in Mall Y gave result 3,13 – 12,5 ppm with average value in 7,88 ± 4,36 ppm. Correlation value between CO concentration and its intake in Mall X area was 0,9983, meanwhile correlation value between CO concentration and its intake in Mall Y area was 0,9903. It was concluded that CO gas concentration measured in parking man’s breathing zone influenced the differences of CO intake value in significance value.

PENGARUH PERSONAL BACKGROUND, POLITICAL BACKGROUND DAN PENGEATAHUAN DEWAN TENTANG ANGGARAN TERHADAP PERAN DPRD DALAM PENGAWASAN KEUANGAN DAERAH

2008 ◽  
Author(s):  
Yesi Mutia Basri ◽  
Rosliana Rosliana
Keyword(s):  
Multivariate Analysis ◽  
Significant Influence ◽  
Political Activity ◽  
Primary Data ◽  
Financial Control ◽  
Independent Variables ◽  
Dependent Variables ◽  
Political Background ◽  
Activity Dependent

This research aim to examine the influence of personal background, political background, and council budget knowledge towards the role of DPRD on region financial control. This research is motivated by the fact that individual background will effect to individual behavior on political activity. Dependent variables in this research are personal background, political background, and council budges knowledge towards the role of DPRD on region financial control Independent variables are the role of DPRD on region financial control in planning, implementing, and responsibility steps. The data in this research consist of primary data that taken from questionnaires distributed directly to respondents. The collected are from 34 Respondents that members of DPRD at Pekanbaru. Hypothesis of this research are examine by using Multivariate Analysis of Variances (MANOVA). The result of this research HI personal background political background and budget knowledge have significant influence toward the role of DPRD on region financial control in planning steps.H2 personal background, politico I background and budget knowledge have no significant influence toward the role of DPRD on region financial control in Implementing steps. H3 personal background political background and budget knowledge have no significant influence toward the role of DPRD on region financial control in Controlling steps.

Two mechanisms for accelerated diffusion of COVID-19 outbreaks in regions with high intensity of population and polluting industrialization: the air pollution-to-human and human-to-human transmission dynamics (Preprint)

2020 ◽  
Author(s):  
Mario Coccia
Keyword(s):  
Air Pollution ◽  
Wind Speed ◽  
Critical Role ◽  
Meteorological Condition ◽  
Meteorological Conditions ◽  
Lessons Learned ◽  
Transmission Dynamics ◽  
Critical Threshold ◽  
Viral Infectivity ◽  
Low Wind Speed

BACKGROUND Coronavirus disease 2019 (COVID-19) is viral infection that generates a severe acute respiratory syndrome with serious pneumonia that may result in progressive respiratory failure and death. OBJECTIVE This study has two goals. The first is to explain the main factors determining the diffusion of COVID-19 that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats with of accelerated viral infectivity in society. METHODS Correlation and regression analyses on on data of N=55 Italian province capitals, and data of infected individuals at as of April 2020. RESULTS The main results are: o The accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution. o Hinterland cities have average days of exceeding the limits set for PM10 (particulate matter 10 micrometers or less in diameter) equal to 80 days, and an average number of infected more than 2,000 individuals as of April 1st, 2020, coastal cities have days of exceeding the limits set for PM10 equal to 60 days and have about 700 infected in average. o Cities that average number of 125 days exceeding the limits set for PM10, last year, they have an average number of infected individual higher than 3,200 units, whereas cities having less than 100 days (average number of 48 days) exceeding the limits set for PM10, they have an average number of about 900 infected individuals. o The results reveal that accelerated transmission dynamics of COVID-19 in specific environments is due to two mechanisms given by: air pollution-to-human transmission and human-to-human transmission; in particular, the mechanisms of air pollution-to-human transmission play a critical role rather than human-to-human transmission. o The finding here suggests that to minimize future epidemic similar to COVID-19, the max number of days per year in which cities can exceed the limits set for PM10 or for ozone, considering their meteorological condition, is less than 50 days. After this critical threshold, the analytical output here suggests that environmental inconsistencies because of the combination between air pollution and meteorological conditions (with high moisture%, low wind speed and fog) trigger a take-off of viral infectivity (accelerated epidemic diffusion) with damages for health of population, economy and society. CONCLUSIONS Considering the complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity, lessons learned for COVID-19 have to be applied for a proactive socioeconomic strategy to cope with future epidemics, especially an environmental policy based on reduction of air pollution mainly in hinterland zones of countries, having low wind speed, high percentage of moisture and fog that create an environment that can damage immune system of people and foster a fast transmission of viral infectivity similar to the COVID-19. CLINICALTRIAL not applicable

scholarly journals Development of a Line Source Dispersion Model for Gaseous Pollutants by Incorporating Wind Shear near the Ground under Stable Atmospheric Conditions

2020 ◽  
Vol 4 (1) ◽  
pp. 17
Author(s):  
Saisantosh Vamshi Harsha Madiraju ◽  
Ashok Kumar
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Wind Shear ◽  
Dispersion Model ◽  
Near Field ◽  
Ground Level ◽  
Gaseous Pollutants ◽  
Air Pollution Control ◽  
Mobile Sources ◽  
Input Variables

Transportation sources are a major contributor to air pollution in urban areas. The role of air quality modeling is vital in the formulation of air pollution control and management strategies. Many models have appeared in the literature to estimate near-field ground level concentrations from mobile sources moving on a highway. However, current models do not account explicitly for the effect of wind shear (magnitude) near the ground while computing the ground level concentrations near highways from mobile sources. This study presents an analytical model based on the solution of the convective-diffusion equation by incorporating the wind shear near the ground for gaseous pollutants. The model input includes emission rate, wind speed, wind direction, turbulence, and terrain features. The dispersion coefficients are based on the near field parameterization. The sensitivity of the model to compute ground level concentrations for different inputs is presented for three different downwind distances. In general, the model shows Type III sensitivity (i.e., the errors in the input will show a corresponding change in the computed ground level concentrations) for most of the input variables. However, the model equations should be re-examined for three input variables (wind velocity at the reference height and two variables related to the vertical spread of the plume) to make sure that that the model is valid for computing ground level concentrations.

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