scholarly journals Effects of a Vessel Speed Reduction Program on Air Quality in Port Areas: Focusing on the Big Three Ports in South Korea

2021 ◽  
Vol 9 (4) ◽  
pp. 407
Author(s):  
Jiyoung An ◽  
Kiyoul Lee ◽  
Heedae Park
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Measurement Data ◽  
Quality Measurement ◽  
Fixed Effect Model ◽  
Ordinary Least Squares ◽  
Speed Reduction ◽  
Policy Effect ◽  
Port Areas ◽  
Vessel Speed

As the seriousness of air pollution from ports and ships is recognized, the Korean Port Authority is implementing many policies and instruments to reduce air pollution in port areas. This study aims to verify the effects of the vessel speed reduction (VSR) program among the procedures related to air pollution in port areas. This study was conducted using panel data created by combining ship entry and departure data and air quality measurement data. We measured the changes in air quality according to the entry and departure of ships and examined whether it changes due to the VSR program. For estimation, the panel fixed-effect model and the ordinary least squares (OLS) model were used. The results suggest that the VSR program had a positive effect on improving air quality in port areas. However, the VSR program’s effects were different over ports. Busan Port showed the highest policy effect, and Incheon Port showed a relatively low policy effect. Based on the results of this study, to maximize the VSR program’s effectiveness at the port, it is necessary to implement other eco-friendly policies as well.

scholarly journals THE EFFECTS OF ENVIRONMENTAL ISSUES ON RESIDENTIAL HOUSE PRICE (IN THE CASE OF ULAANBAATAR CITY)

2018 ◽  
pp. 117-130
Author(s):  
Oyunjargal D ◽  
Byambatseren Ch
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
House Price ◽  
Measurement Data ◽  
Quality Measurement ◽  
Air Quality Index ◽  
Quality Level ◽  
Average Price ◽  
Residential House ◽  
The Impact

The purpose of this research is to determine the impact of the environment, especially the quality of air on house price. In addition, it also includes the research of the linkage between the index of air quality and average price of residential house which located in the most crowded districts of Ulaanbaatar such as Bayangol, Bayanzurkh, Chingeltei, Sukhbaatar, Songinokhairkhan and Khan-Uul. The statistical analysis and statistics determination methods were applied to identify the relationship utilizing the air quality index, determined from the air quality measurement data recorded in 2015-2017, and the average price per square meter of newly built apartment houses in the selected districts. The research findings suggest that there is little direct link between the house prices and air quality level, and the air quality levels of Ulaanbaatar districts do not have a significant impact on the price per square meter. Therefore, the air quality index should not considered as a house price determinant.

scholarly journals Evaluation of the impact of meteorological conditions on the amount of air pollution in Krakow

2019 ◽  
Vol 108 ◽  
pp. 02012
Author(s):  
Małgorzata Piaskowska-Silarska ◽  
Krzysztof Pytel ◽  
Stanisław Gumuła ◽  
Wiktor Hudy
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Measurement Data ◽  
Ecological Monitoring ◽  
Meteorological Conditions ◽  
Pollution Problem ◽  
Air Quality Improvement ◽  
Meteorological Elements ◽  
The Impact ◽  
The Relationship

Abstract. The publication presents an assessment of the impact of meteorological conditions on air quality in a given location. The subject matter of the work is related to problem-review issues in the field of environmental protection and energy management. The publication draws attention to the fact that despite several decades of ecological monitoring of air pollution, only in recent years attention has been paid to the scale of air pollution problem. The study examined the relationship between meteorological elements (wind velocity, relative humidity on the amount of air pollution immissions. Significant impact of precipitation, atmospheric pressure and thermal braking layer was indicated. The possibilities of air quality improvement were presented based on the measurement data concerning the immission of impurities.

scholarly journals Investigating the regional contributions to air pollution in Beijing: a dispersion modelling study using CO as a tracer

2020 ◽  
Vol 20 (5) ◽  
pp. 2825-2838 ◽  
Author(s):  
Marios Panagi ◽  
Zoë L. Fleming ◽  
Paul S. Monks ◽  
Matthew J. Ashfold ◽  
Oliver Wild ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Measurement Data ◽  
Industrial Sector ◽  
Transport Processes ◽  
Control Measures ◽  
Emission Inventories ◽  
Dispersion Modelling ◽  
Rapid Urbanization ◽  
Air Masses

Abstract. The rapid urbanization and industrialization of northern China in recent decades has resulted in poor air quality in major cities like Beijing. Transport of air pollution plays a key role in determining the relative influence of local emissions and regional contributions to observed air pollution. In this paper, dispersion modelling (Numerical Atmospheric Modelling Environment, NAME model) is used with emission inventories and in situ ground measurement data to track the pathways of air masses arriving in Beijing. The percentage of time the air masses spent over specific regions during their travel to Beijing is used to assess the effects of regional meteorology on carbon monoxide (CO), a good tracer of anthropogenic emissions. The NAME model is used with the MEIC (Multi-resolution Emission Inventory for China) emission inventories to determine the amount of pollution that is transported to Beijing from the immediate surrounding areas and regions further away. This approach captures the magnitude and variability of CO over Beijing and reveals that CO is strongly driven by transport processes. This study provides a more detailed understanding of relative contributions to air pollution in Beijing under different regional airflow conditions. Approximately 45 % over a 4-year average (2013–2016) of the total CO pollution that affects Beijing is transported from other regions, and about half of this contribution comes from beyond the Hebei and Tianjin regions that immediately surround Beijing. The industrial sector is the dominant emission source from the surrounding regions and contributes over 20 % of the total CO in Beijing. Finally, using PM2.5 to determine high-pollution days, three pollution classification types of pollution were identified and used to analyse the APHH winter campaign and the 4-year period. The results can inform targeted control measures to be implemented by Beijing and the surrounding provinces to tackle air quality problems that affect Beijing and China.

scholarly journals Functionality of openair package in air pollution assessment and modeling — a case study of Krakow

2017 ◽  
Vol 28 (2) ◽  
pp. 22-27 ◽  
Author(s):  
Adriana Szulecka ◽  
Robert Oleniacz ◽  
Mateusz Rzeszutek
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Management System ◽  
Measurement Data ◽  
Pollution Assessment ◽  
Software Modeling ◽  
Pollution Dispersion ◽  
Back Trajectories ◽  
Air Pollution Dispersion ◽  
Air Pollution Assessment

Abstract The paper presents the possibilities of selected functions from openair package for R programming environment in urban air pollution assessment. Examples of data analysis were based on the measurements from continuous air quality monitoring stations in Krakow (Poland). In order to present additional functionality of this software, modeling results of back trajectories and air pollution dispersion were used. Functions and visualization methods included in openair package make scrutiny of large data sets easier and less time consuming. They allow for analysis of measurement data with the determination of general relationships between parameters, additional complex spatial analyses for back trajectories, and validation of air pollution dispersion models. Openair package is, therefore, a valuable and functional tool that can be successfully used as a support in the air quality management system.

scholarly journals Multi-Horizon Air Pollution Forecasting with Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1235
Author(s):  
Mirche Arsov ◽  
Eftim Zdravevski ◽  
Petre Lameski ◽  
Roberto Corizzo ◽  
Nikola Koteli ◽  
...  
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Short Term Memory ◽  
Moving Average ◽  
Arima Model ◽  
Measurement Data ◽  
Quality Measurement ◽  
Capital City ◽  
Industrial Plants ◽  
Proactive Measures

Air pollution is a global problem, especially in urban areas where the population density is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and waste. North Macedonia, as a developing country, has a serious problem with air pollution. The problem is highly present in its capital city, Skopje, where air pollution places it consistently within the top 10 cities in the world during the winter months. In this work, we propose using Recurrent Neural Network (RNN) models with long short-term memory units to predict the level of PM10 particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from sensors placed at multiple locations in Skopje and meteorological conditions such as temperature and humidity. We compare different deep learning models’ performance to an Auto-regressive Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models consistently outperform the baseline model and can be successfully employed for air pollution prediction. Ultimately, we demonstrate that these models can help decision-makers and local authorities better manage the air pollution consequences by taking proactive measures.

scholarly journals Air Pollution Dispersion Modeling in Port Areas

2020 ◽  
Vol 3 (3) ◽  
pp. 157-170
Author(s):  
Teodora Milošević ◽  
Lado Kranjčević ◽  
Stjepan Piličić ◽  
Marko Čavrak ◽  
Igor Kegalj ◽  
...  
Keyword(s):  
Decision Making ◽  
Air Pollution ◽  
Air Quality ◽  
Ambient Air ◽  
Dispersion Modeling ◽  
Plume Dispersion ◽  
Activity Data ◽  
Pollution Dispersion ◽  
Horizon 2020 ◽  
Port Areas

For the last couple of decades, environmental protection awareness within port areas is gaining ever more importance. Ports can have a tremendous impact on the environment, especially in terms of air pollution. The main pollution sources are various port activities such as road and rail traffic, cargo handling and marine vessel operations. Air quality models can be of great help in estimating the effect on the ambient air quality from one or more sources emitting pollutants to the atmosphere. One of those models is the widely used Gaussian Plume dispersion approach. Based on existing measurements and port activity data, models can simulate the dispersion of air pollutants caused by activities and operations taking place within the port. By using historical data, they can simulate the current state of the air quality in the port and with the help of weather predictions simulate possible future situation. Simulations can assist the port manager/operator in the decision-making process in order to optimize various activities within the port and minimize their impact on the environment. One of the main objectives of the Horizon 2020 Project PIXEL (Port IoT for environmental leverage) is the deployment of environmental pollution models which can aid in the decision-making processes within the port domain. This paper reviews the current advances in the field of air pollution modelling with a special emphasis on port scenarios.

scholarly journals Air Quality Measurement using Computer Vision and CCTV Footage of Road Traffic

2020 ◽  
Vol 8 (6) ◽  
pp. 4177-4181
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Road Traffic ◽  
Quality Measurement ◽  
Indian Government ◽  
Vehicular Pollution ◽  
Pollution Levels ◽  
Robust Model ◽  
Video Feed ◽  
Monitoring Stations

Air Quality is at a steady state of decline throughout the world. While the Indian government, in particular, has been deploying monitoring stations across multiple cities to not only monitor but also establish a cause and effect relationship when it comes to air pollution, these monitoring stations clearly, don’t suffice the actual demands for building a robust model for Air Quality Index. Our goal here is to reduce costs in terms of hardware deployment while, at the same time, provide a higher number of data points of collection on pre-existing infrastructure. The project aims at calculating the air pollution factors at the suburban level using Vehicular Emissions. The idea is to identify the number and type of vehicles from a video feed and then estimate the vehicular pollution levels using the data collected.

scholarly journals Embedded Generative Air Pollution Model with Variational Autoencoder and Environmental Factor Effect in Ulaanbaatar City

Atmosphere ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 71
Author(s):  
Bulgansaikhan Baldorj ◽  
Munkherdene Tsagaan ◽  
Lodoysamba Sereeter ◽  
Amanjol Bulkhbai
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Environmental Factors ◽  
Influencing Factors ◽  
Quality Measurement ◽  
Temporal Distribution ◽  
Range Data ◽  
Pollution Levels ◽  
Variational Autoencoder ◽  
Wide Range

Air pollution is one of the most pressing modern-day issues in cities around the world. However, most cities have adopted air quality measurement devices that only measure the past pollution levels without paying attention to the influencing factors. To obtain preliminary pollution information with regard to environmental factors, we developed a variational autoencoder and feedforward neural network-based embedded generative model to examine the relationship between air quality and the effects of environmental factors. In the model, actual SO2, NO2, PM2.5, PM10, and CO measurements from 2016 to 2020 were used, which were assembled from 15 differently located ground monitoring stations in Ulaanbaatar city. A wide range of weather and fuel measurements were used as the data for the influencing factors, and were collected over the same period as the air pollution data were recorded. The prediction results concerned all measurement stations, and the results were visualized as a spatial–temporal distribution of pollution and the performance of individual stations. A cross-validated R2 was used to estimate the entire pollution distribution through the regions as SO2: 0.81, PM2.5: 0.76, PM10: 0.89, and CO: 0.83. Pearson’s chi-squared tests were used for assessing each measurement station, and the contingency tables represent a high correlation between the actual and model results. The model can be applied to perform specific analysis of the interdependencies between pollution and environmental factors, and the performance of the model improves with long-range data.

scholarly journals Performance evaluation of portable air quality measurement system using raspberry pi for remote monitoring

2020 ◽  
Vol 17 (2) ◽  
pp. 564 ◽  
Author(s):  
Muhammad Farhan Mohd Pu’ad ◽  
Teddy Surya Gunawan ◽  
Mira Kartiwi ◽  
Zuriati Janin
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Measurement System ◽  
Quality Measurement ◽  
Pollution Index ◽  
Ground Level ◽  
Well Being ◽  
Good Health ◽  
Mobile Internet ◽  
Raspberry Pi

<span>United Nations’ Sustainable Development Goals focuses on good health and well-being for all. Air pollution becomes a huge threat to delivering on the vision of a better world and related at least to Goal 3, 7, 11, and 13. In Malaysia, air pollution index were monitored on 68 locations. The Department of Environment monitors air quality using costly continuous air quality monitoring stations (CAQMs) installed at fixed locations of highly populated and industrial areas. The objective of this paper is to develop a portable air quality measurement system which can measure particulate matters (PM) smaller than 10 and 2.5 microns, and four hazardous gasses, including carbon monoxide, sulphur dioxide, ground level ozone and nitrogen dioxide, as well as humidity and temperature. Six sensors were used and validated using several rigorous experiments. The functionality of the system was evaluated by measuring sub-API readings in areas with low and high traffic volumes. Experimental results showed that the proposed system was highly responsive and able to detect the types and concentrations of air pollutants instantly. Furthermore, equipped with the mobile internet, geo-tagged GPS location and web server on Raspberry Pi, the developed portable system could be accessed remotely.</span>

scholarly journals Ambient measurement of shipping emissions in Shanghai port areas

2018 ◽  
Author(s):  
Xinning Wang ◽  
Yin Shen ◽  
Yanfen Lin ◽  
Jun Pan ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Measurement ◽  
Ambient Air ◽  
Throughput Capacity ◽  
High Temporal Resolution ◽  
Port Area ◽  
Local Air Pollution ◽  
Fine Size ◽  
Negative Impacts ◽  
Port Areas

Abstract. Growing shipping activities in port areas have generated negative impacts on climate, air quality and human health. To better evaluate the environmental impact of shipping emissions, ambient air quality measurement was carried out at Shanghai port in the summer of 2016. Large throughput capacity and busy shipping traffics of Shanghai port make it an ideal place to characterize shipping emissions. Gaseous (NO, NO2, SO2, O3) and particulate concentrations (PM2.5), particle sizes and chemical composition of individual shipping emission particles were continuously monitored for 3 months. High temporal resolution data show that shipping emissions is a major culprit of local air pollution problems. Distinct shipping emission plumes were observed using online measurement in port area. The SO2 and Vanadium particles numbers were found to correlate best with shipping emissions in Shanghai port. Single particle mass spectra of fresh shipping emission were identified based on the dominant peaks of Sulfate, EC and indicative metals of V, Ni, Fe and Ca, and nitrate peaks in aged particles. Fresh shipping emission particles mainly concentrated in ultra-fine size range where their number contributions are more apparent than their mass. For the coastal port it is found appropriate to separate shipping emissions from land-based emissions by prevalent wind directions. Advanced measurement conducted in the present study show that in port region shipping emissions contributed 36.4 % SO2, 0.7 % NO, 5.1 % NO2, −0.9 % O3, 5.9 % PM2.5, 49.5 % Vanadium particles if land-based emissions were included, and 57.2 % SO2, 71.9 % NO, 30.4 % NO2, −16.6 % O3, 27.6 % PM2.5, 77.0 % Vanadium particles if land-based emissions were excluded.

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