An Urban Air Pollution Early Warning System Based on PM2.5 Prediction Applied in Ploiesti City

2017 ◽  
Vol 68 (4) ◽  
pp. 858-863
Author(s):  
Mihaela Oprea ◽  
Marius Olteanu ◽  
Radu Teodor Ianache
Keyword(s):  
Air Pollution ◽  
Early Warning ◽  
Urban Areas ◽  
Early Warning System ◽  
Urban Air Pollution ◽  
Warning System ◽  
Air Pollutant ◽  
Pollution Level ◽  
Urban Air ◽  
Prediction Approach

Fine particulate matter with a diameter less than 2.5 �m (i.e. PM2.5) is an air pollutant of special concern for urban areas due to its potential significant negative effects on human health, especially on children and elderly people. In order to reduce these effects, new tools based on PM2.5 monitoring infrastructures tailored to specific urban regions are needed by the local and regional environmental management systems for the provision of an expert support to decision makers in air quality planning for cities and also, to inform in real time the vulnerable population when PM2.5 related air pollution episodes occur. The paper focuses on urban air pollution early warning based on PM2.5 prediction. It describes the methodology used, the prediction approach, and the experimental system developed under the ROKIDAIR project for the analysis of PM2.5 air pollution level, health impact assessment and early warning of sensitive people in the Ploiesti city. The PM2.5 concentration evolution prediction is correlated with PM2.5 air pollution and health effects analysis, and the final result is processed by the ROKIDAIR Early Warning System (EWS) and sent as a message to the affected population via email or SMS. ROKIDAIR EWS is included in the ROKIDAIR decision support system.

scholarly journals A Novel Air Quality Early-Warning System Based on Artificial Intelligence

2019 ◽  
Vol 16 (19) ◽  
pp. 3505
Author(s):  
Mo ◽  
Zhang ◽  
Li ◽  
Qu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Prediction Model ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Air Pollutant ◽  
Concentration Data ◽  
Whale Optimization ◽  
The World

The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

scholarly journals Algorithm to Predict the Rainfall Starting Point as a Function of Atmospheric Pressure, Humidity, and Dewpoint

Climate ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 131
Author(s):  
Alfonso Gutierrez-Lopez ◽  
Ivonne Cruz-Paz ◽  
Martin Muñoz Mandujano
Keyword(s):  
Early Warning ◽  
Atmospheric Pressure ◽  
Urban Areas ◽  
Early Warning System ◽  
Warning System ◽  
Forecast Model ◽  
Flood Damage ◽  
Precipitation Forecast ◽  
Rainfall Forecasting ◽  
Starting Point

Forecasting extreme precipitations is one of the main priorities of hydrology in Latin America and the Caribbean (LAC). Flood damage in urban areas increases every year, and is mainly caused by convective precipitations and hurricanes. In addition, hydrometeorological monitoring is limited in most countries in this region. Therefore, one of the primary challenges in the LAC region the development of a good rainfall forecasting model that can be used in an early warning system (EWS) or a flood early warning system (FEWS). The aim of this study was to provide an effective forecast of short-term rainfall using a set of climatic variables, based on the Clausius–Clapeyron relationship and taking into account that atmospheric water vapor is one of the variables that determine most meteorological phenomena, particularly regarding precipitation. As a consequence, a simple precipitation forecast model was proposed from data monitored at every minute, such as humidity, surface temperature, atmospheric pressure, and dewpoint. With access to a historical database of 1237 storms, the proposed model allows use of the right combination of these variables to make an accurate forecast of the time of storm onset. The results indicate that the proposed methodology was capable of predicting precipitation onset as a function of the atmospheric pressure, humidity, and dewpoint. The synoptic forecast model was implemented as a hydroinformatics tool in the Extreme Precipitation Monitoring Network of the city of Queretaro, Mexico (RedCIAQ). The improved forecasts provided by the proposed methodology are expected to be useful to support disaster warning systems all over Mexico, mainly during hurricanes and flashfloods.

scholarly journals Comparison of the risk perception and warning response to air pollution on an individual level in China

2021 ◽  
Vol 252 ◽  
pp. 03063
Author(s):  
Fangping Wang ◽  
Fei Su
Keyword(s):  
Air Pollution ◽  
Risk Perception ◽  
Early Warning ◽  
Public Perception ◽  
Warning System ◽  
Pollution Level ◽  
Individual Level ◽  
The Public ◽  
Gender Education ◽  
Warning Response

Individual responses to China’s heavy air pollution early warning are poorly understood. This knowledge gap has hampered the evaluation and improvement of the early warning system in providing the targeted populations with effective protection guidance. In order to explore the public’s response to air pollution warning, field survey were conducted in three major cities of China in 2016. The results indicated that different levels of air pollution warnings were correctly understood in these three cities, but the warning response rate was low. Significant differences in the public’s risk perception were demonstrated. Public perception of the health impacts of air pollution (HEP) and knowledge of the warning index (AQI) were significantly higher in Beijing than in Shenzhen. The public perception of the pollution level (DEVIATION) was equal in Beijing and Shenzhen, but higher than that in Shanghai. Gender, education, and risk perception were crucial factors influencing the public’s willingness to respond to warnings. Early warning policymakers can use this research to optimize the design and dissemination of early warning information to improve the public’s health and quality of life in cities with air pollution.

scholarly journals The Relationship between Urban Air Pollution and Urban Planning Decisions

2018 ◽  
Vol 3 (11) ◽  
pp. 181 ◽  
Author(s):  
Nur Sinem Ozcan ◽  
K. Mert Cubukcu
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Fossil Fuels ◽  
Publishing House ◽  
Urban Air Pollution ◽  
Urban Air ◽  
Pollution Levels ◽  
Planning Decisions ◽  
Diffusion Tubes ◽  
Pollution Problem

The air pollution problem remains, although significant improvements have been seen in urban air quality over the last years. This study aims to show that the spatial statistic techniques can well be used to examine and explain the air pollution levels in urban areas. The data for SO2 and NO2 concentrations are measures using passive diffusion tubes at the 67 monitoring sites in the district of Çiğli (Izmir). The tubes were exposed for a 2-week period in August 2015. For the determination of the level of clustering for high values and low values of pollutants, Getis-Ord G* local statistics are calculated. There are five points with high values of SO2 surrounded by low values, three points of NO2, where the results are statistically significant at the 0.10 level. The presence of the industrial zone, the form of fossil fuels used in heating, and topography are strong determinants urban air pollution.Keywords: Urban air pollution, planning decisions, spatial autocorrelation, air pollutants eISSN 2398-4279 © 2018. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. https://doi.org/10.21834/ajqol.v3i11.134

scholarly journals Forecast of urban air pollution level by expertise

2017 ◽  
Vol 17 (4) ◽  
pp. 144-150
Author(s):  
Е. О. Lazareva ◽  
◽  
I. N. Lipovitskaya ◽  
Е. S. Аndreeva ◽  
Y. V. Yefimova ◽  
...  
Keyword(s):  
Air Pollution ◽  
Urban Air Pollution ◽  
Pollution Level ◽  
Urban Air

scholarly journals THE PREDICTIVE EFFECTS OF PROTECTION MOTIVATION THEORY ON ADAPTIVE BEHAVIOUR TOWARDS URBAN AIR QUALITY

2019 ◽  
Vol 5 (1) ◽  
pp. 69-74
Author(s):  
Syazwani Sahrir
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Urban Air Pollution ◽  
Road Transport ◽  
Protection Motivation Theory ◽  
Adaptive Behaviour ◽  
Urban Air ◽  
Protection Motivation ◽  
Motivation Theory ◽  
Behavioural Responses

In urban areas, the rigid division of residential, commercial, employment and recreational areas forms a reliance on road transport, which leads to high levels of emission that gradually affects the quality of the urban environment. We establish the Protection Motivation Theory (PMT) as a framework for explaining adaptive behavioural responses among urban communities in Malaysia. Participants (N = 450) answered to face-to-face questionnaire survey, and the results specify establishment for the proposed model, with perceived vulnerability (H1) (ß = 0.246, t = 4.534, P=0.000) and and self-efficacy (H3) (ß = 0.510, t = 9.653, P=0.000) positively predicting adaptive behaviour on  urban air pollution. The results presented that these structures were able to predict 47% of the variance of adaptive behaviour. The study establishes a significant contribution to the literature by contributing an indication of PMT as an ideal framework for adaptive behavioural responses on urban air pollution.

scholarly journals Examination of the Relationship between Urban Air Pollution and Urban Planning Decisions in Cigli Case, Izmir (Turkey)

2016 ◽  
Vol 1 (2) ◽  
pp. 178 ◽  
Author(s):  
Nur Sinem Ozcan ◽  
K. Mert Cubukcu
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Fossil Fuels ◽  
Publishing House ◽  
Urban Air Pollution ◽  
Systematic Sampling ◽  
Urban Air ◽  
Pollution Levels ◽  
Planning Decisions ◽  
Diffusion Tubes

The air pollution problem remains, although significant improvements have been seen in urban air quality over the last years. Despite the size and variety of studies on urban air pollution, the usage of spatial statistics has been extremely limited. This study aims to show that the spatial statistic techniques can well be used to examine and explain the air pollution levels in urban areas. The data for the sulphur dioxide (SO2) and nitrogen dioxide (NO2) concentrations are measures using passive diffusion tubes at the 67 monitoring sites in the district of Çiğli (Izmir), which are selected through a spatial systematic sampling process. The tubes were exposed for a 2-week period in August 2015. For the determination of the level of clustering for high values and low values of SO2 and NO2 concentrations, Getis-Ord G* local statistics are calculated. There are five points with high values of SO2 surrounded by low values, three points of NO2, where the results are statistically significant at the 0.10 level. The findings indicate that the presence of the industrial zone, the form of fossil fuels (coal or natural gas) used in heating, and topography are the strong determinants urban air pollution.© 2016. The Authors. Published for AMER ABRA by e-International Publishing House, Ltd., UK. Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.Keywords: Urban air pollution; planning decisions; spatial autocorrelation; air pollutants

scholarly journals The Impacts of Urban Air Pollution Emission Density on Air Pollutant Concentration Based on a Panel Model

2020 ◽  
Vol 12 (20) ◽  
pp. 8401
Author(s):  
Jong In Baek ◽  
Yong Un Ban
Keyword(s):  
Air Pollution ◽  
Population Density ◽  
Urban Air Pollution ◽  
Air Pollutant ◽  
Spatial Density ◽  
Urban Air ◽  
Emission Sources ◽  
Total Emission ◽  
Panel Model ◽  
The Impact

In terms of urban planning, the impact of urbanization and high density on the environment is a major issue. This study intended to analyze the effect of spatial density characteristics of urban air pollution sources on urban air pollution concentration using a panel model. As the total population density, the number of cars registered per capita, and the total emission facility density increased, together with a closer distance to a thermal power plant, the nitrogen dioxide(NO2) concentration increased. Net population density was also found to have the greatest impact on the structure and density of emission sources of ozone(O3) followed by the number of cars registered per person and the total emission facility density. It was confirmed that particular matter(PM10) concentrations are strongly influenced in positive directions by the spatial density characteristics of emission sources that show significant differences between regions.

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