scholarly journals ENVIRONMENTAL POLLUTION: EFFECT ON PEDESTRIANS WHILE WALKING IN URBAN STREET

2022 ◽  
Vol 4 (1) ◽  
pp. 18-29
Author(s):  
Adepoju Onifade ◽  
Babatunde Folasayo ◽  
Abimbola Babatunde
Keyword(s):  
Air Quality ◽  
Local Government ◽  
Environmental Pollution ◽  
Local Governments ◽  
Quality Index ◽  
Noise Pollution ◽  
The State ◽  
Air Quality Index ◽  
Tree Planting ◽  
The Impact

Purpose:  The reason for this study is because of observed difference in environmental condition in Lagos metropolis.  The change is witnessed in environmental change arising from air, water and noise pollution mostly from increasing vehicle emissions in the State. This study has been conducted to analyze the environmental effects of pollution on pedestrians. Specific objectives are determine the air quality of the city at most populate headquarters of each of the 20 Local Government Areas of Lagos State, to examine the impact of pollution (air, water and noise) on pedestrians and assess various measures for reducing environmental pollution in the State. Methodology: The use of Thermo scientific MIE pDR-1500 instrument was used to measure air quality index of the selected locations and survey was carried out with well-structured questionnaire to elicit information with the aid of incidental sampling technique on impact of pollution on pedestrians from 177 respondents. Findings: Air Quality Index was shown with histogram chart where six out of 20 Local Government Areas are above the acceptable standard of pollution. There is rising cases of pollution in the State and very few Local governments were within acceptable range. One –Sample T-test showed that air pollution is majorly affecting pedestrians with t-value of 22.226 followed by noise with 19.643 and water with 5.529 respectively. Conclusion and recommendations: The research concluded that, there is need to control the rising cases of pollution in the state and policies to tame air and noise pollution in the state should be adopted. Emission control strategies to be adopted with the existing ones can be in form of restricting hours of movement of vehicles to late at night to avoid human pollutant contact, encourage tree planting and rapid evacuation of environmental waste.

scholarly journals Predicting the Air Quality Index of Industrial Areas in an Industrialized City in India Using Adopting Markov Chain Model

2020 ◽  
Author(s):  
Raja Prasad S.V.S ◽  
Vishnu Namboodiri V
Keyword(s):  
Markov Chain ◽  
Air Quality ◽  
Quality Index ◽  
Markov Chain Model ◽  
Industrial Area ◽  
The State ◽  
Air Quality Index ◽  
Chain Model ◽  
Industrial Areas ◽  
The Impact

Introduction: The rapid urbanization coupled with industrial development in Indian cities has led to air pollution that causes adverse effects on the health of human beings. So, it is crucial to track the quality of air in industrial areas of a city to insulate the public from harmful air pollutants.  The present study examined and predicted air quality index levels in industrial areas located in Hyderabad, India. Materials and Methods: Markov chain model was developed to predict the air quality index levels in three industrial areas of Hyderabad city. The secondary data pertaining to the air quality index was analyzed from January, 2016 to December 2019 by developing Markov chain model. The state transition probabilities were used to find the predicted probability for the next 4 years. The study also analyzed the mean return time for specific states. Results: According to the findings, the highest frequency observed for transition in a month to the next month was 31 for the second industrial area in moderate state. The longest time required to repeat the state was 23.585 months and 23.259 months for the industrial area 3. Conclusions: Air quality index varies in industrial areas depending on the nature of industries and type of emissions. The prediction of air quality index is useful for the local authorities to implement measures to minimize the impact of pollutants on human health.

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 The impact of air quality on its Baidu index: grey model analysis

2021 ◽  
Author(s):  
Leping Tu ◽  
Yan Chen
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Grey Correlation ◽  
Convolution Model ◽  
Grey Correlation Analysis ◽  
And Control ◽  
The Impact ◽  
The Relationship

Abstract To investigate the relationship between air quality and its Baidu index, we collect the annual Baidu index of air pollution hazards, causes and responses. Grey correlation analysis, particle swarm optimization and grey multivariate convolution model are used to simulate and forecast the comprehensive air quality index. The result shows that the excessive growth of the comprehensive air quality index will lead to an increase in the corresponding Baidu index. The number of search for the causes of air quality has the closest link with the comprehensive air quality index. Strengthening the awareness of public about air pollution is conducive to the improvement of air quality. The result provides a reference for relevant departments to prevent and control air pollution.

Impacts on Energy and Environmental Efficiency by Media Report – Modifies Undesirable Dynamic DEA

2020 ◽  
Author(s):  
Ying Li ◽  
Tai-Yu Lin ◽  
Yung-ho Chiu ◽  
Huaming Chen ◽  
Hongyi Cen
Keyword(s):  
Economic Growth ◽  
Air Quality ◽  
Environmental Pollution ◽  
Environmental Protection ◽  
News Media ◽  
Media Coverage ◽  
Quality Index ◽  
Environmental Efficiency ◽  
Air Quality Index ◽  
Media Reports

Abstract Background: Rapid economic growth in China has resulted in a commensurate increase in energy consumption, which in turn has caused an increase in environmental pollution problems. Past research has mainly focused on energy and environmental efficiency analyses with little consideration of the influence of media influence on environmental protection. Further, most studies have used static models and have ignored the dynamic changes over time. Methods : To go some way to filling this research gap, this study developed a modified undesirable Dynamic DEA model that included air quality index (AQI) and CO2 indicators to explore the relationship between energy, the environment and media efficiency in 31 Chinese cities from 2013 to 2016. Results: It was found that: 1. Chongqing, Guangzhou, Nanjing and Shanghai had efficiencies of 1, but Lanzhou, Shijiazhuang, Taiyuan, Xining and Yinchuan needed significant improvements; 2. while Chongqing, Guangzhou, Kunming, Nanning and Shanghai had relatively high media efficiency, the other cities had low efficiency and required improvements; 3. the CO 2 emissions efficiency in most cities was better than the air quality index efficiency; and 4. media reports in most cities were found to have a more positive impact on CO 2 emissions efficiency than AQI efficiency. Conclusions: As environmental awareness enhances the health of civilians and promotes economic growth, the news media needs to promote environmental protection, and should increase its environmental pollution coverage. The quality of media reports on environmental pollution and especially on air pollution need to be improved. Therefore, environmental pollution and awareness media coverage needs to be increased.

scholarly journals A psychophysical measurement on subjective well-being and air pollution

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Yuan Li ◽  
Dabo Guan ◽  
Yanni Yu ◽  
Stephen Westland ◽  
Daoping Wang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Well Being ◽  
Air Quality Index ◽  
Subjective Well Being ◽  
Atmospheric Conditions ◽  
Survey Techniques ◽  
Physical Effects ◽  
The Impact

AbstractAlthough the physical effects of air pollution on humans are well documented, there may be even greater impacts on the emotional state and health. Surveys have traditionally been used to explore the impact of air pollution on people’s subjective well-being (SWB). However, the survey techniques usually take long periods to properly match the air pollution characteristics from monitoring stations to each respondent’s SWB at both disaggregated spatial and temporal levels. Here, we used air pollution data to simulate fixed-scene images and psychophysical process to examine the impact from only air pollution on SWB. Findings suggest that under the atmospheric conditions in Beijing, negative emotions occur when PM2.5 (particulate matter with a diameter less than 2.5 µm) increases to approximately 150 AQI (air quality index). The British observers have a stronger negative response under severe air pollution compared with Chinese observers. People from different social groups appear to have different sensitivities to SWB when air quality index exceeds approximately 200 AQI.

Analyzing the impact of heating emissions on air quality index based on principal component regression

2018 ◽  
Vol 171 ◽  
pp. 1577-1592 ◽  
Author(s):  
Han Li ◽  
Shijun You ◽  
Huan Zhang ◽  
Wandong Zheng ◽  
Wai-ling Lee ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Principal Component Regression ◽  
Principal Component ◽  
Air Quality Index ◽  
The Impact

scholarly journals Assessment of AIR Quality Index for Delhi region: A comparison between odd-even policy 2019 and Lock Down Period.

2021 ◽  
Author(s):  
Chesta Dhingra
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Ambient Temperature ◽  
Quality Index ◽  
Winter Season ◽  
Air Quality Index ◽  
Machine Learning Algorithms ◽  
Accurate Estimation ◽  
Pm10 And Pm2.5 ◽  
The Impact

The aim behind doing this research is to analyse the impact of odd-even policy andlockdown implementation on the air quality index of Delhi by doing the case study on the fourregions Ashok Vihar, Anand Vihar, Dwarka and R.K. Puram. The data is been collected fromDPCC and the main parameters we looked for are PM10 and PM2.5. In which we find out that.highest levels of the pollutants PM10 and PM2.5 been observed during the time of odd-evenpolicy implementation for the year 2019 (04 November 2019- 15 November 2019) whereasduring the lockdown period (23 March 2020-31st August 2020) a great decline in pollutantlevels is been detected. This we further try to correlate with the spatial variations of Delhiregion and able to discern that meteorological parameters (Ambient Temperature, RelativeHumidity, Wind Speed and Solar Radiations) in respect with seasonal variations have a majorinfluence on PM 10 and PM 2.5 levels. During the winter season both the parameters PM10& PM2.5 are touching the peak because of the impact of three major meteorological parametersAmbient Temperature, Wind Speed and Solar Radiation and during the monsoon season airquality conditions are quite favourable because of Ambient Temperature and Wind Speedparameters. In the end we use the ensembled machine learning algorithms like Random Forestand Extra trees regressor to have an accurate estimation of PM2.5 levels for all the four regionsof Delhi and perceived that these ensembled learning techniques are better than other machinelearning algorithms like Neural Networks, Linear regression and SVMs. The Random Forestand Extra trees regressor models give the R2value 0.75 and 0.78 respectively for estimation ofPM2.5; R2 value is a statistical measurement which explains the variance of dependent variablebased on the independent variables of a regression model.

scholarly journals Temporal Variations in the Air Quality Index and the Impact of the COVID-19 Event on Air Quality in Western China

2020 ◽  
Vol 20 (7) ◽  
pp. 1552-1568 ◽  
Author(s):  
Jiajia Zhang ◽  
Kangping Cui ◽  
Ya-Fen Wang ◽  
Jhong-Lin Wu ◽  
Wei-Syun Huang ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Temporal Variations ◽  
Air Quality Index ◽  
Western China ◽  
The Impact

scholarly journals Application of Fuzzy Inference System in the Prediction of Air Quality Index

2021 ◽  
Vol 6 (3) ◽  
pp. 75-85
Author(s):  
Nor Hayati Shafii ◽  
Nur Aini Mohd Ramle ◽  
Rohana Alias ◽  
Diana Sirmayunie Md Nasir ◽  
Nur Fatihah Fauzi
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Fuzzy Inference System ◽  
Quality Index ◽  
Fuzzy Inference ◽  
Average Concentration ◽  
Air Quality Index ◽  
Inference System ◽  
Proposed Model ◽  
The Impact

Air pollution is the presence of substances in the atmosphere that are harmful to the health of humans and other living beings. It is caused by solid and liquid particles and certain gases that are suspended in the air.  The air pollution index (API) or also known as air quality index (AQI) is an indicator for the air quality status at any area.  It is commonly used to report the level of severity of air pollution to public and to identify the poor air quality zone.  The AQI value is calculated based on average concentration of air pollutants such as Particulate Matter 10 (PM10), Ozone (O3), Carbon Dioxide (CO2), Sulfur Dioxide (SO2) and Nitrogen Dioxide (NO2).  Predicting the value of AQI accurately is crucial to minimize the impact of air pollution on environment and human health.  The work presented here proposes a model to predict the AQI value using fuzzy inference system (FIS). FIS is the most well-known application of fuzzy logic and has been successfully applied in many fields.  This method is proposed as the perfect technique for dealing with environmental well known and tackling the choice made below uncertainty.  There are five levels or indicators of AQI, namely good, moderate, unhealthy, very unhealthy, and hazardous. This measurement is based on classification made from the Department of Environment (DOE) under the Ministry of Science, Technology, and Innovation (MOSTI). The results obtained from the actual data are compared with the results from the proposed model.  With the accuracy rate of 93%, it shows that the proposed model is meeting the highest standard of accuracy in forecasting the AQI value.

scholarly journals Effects of Environmental Pollution on Changes in Blood Biochemical Parameters

2019 ◽  
Vol 1 (1) ◽  
pp. 18-21
Author(s):  
Muhammad Ibrahim ◽  
Sehrish Noor ◽  
Yasmeen Lashari ◽  
Sabeena Rizwan ◽  
Abdul Rehman ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Environmental Pollution ◽  
Biochemical Parameters ◽  
Quality Index ◽  
Negative Relationship ◽  
Air Quality Index ◽  
Mean Values ◽  
Metabolic Marker ◽  
Temporal Models

  Abstract   A metabolic marker in the blood of individuals exposed to air pollution is plausible as some environmental pollutants have been observed to enter the blood stream directly from lungs. The main objective of the current review is to find out changes in biochemical parameters of human blood as a result of environmental pollution. Spatio temporal models involving measurement of  Air Quality Index were accommodating to find out the results of development of an aggregate air quality index. Results depicted that mean values of RBC and hemoglobin concentration in blood of exposed children were significantly different from those of non-exposed group. Air pollution may adversely affect children's erythrocytes, particularly PM10 have a significant negative relationship with hemoglobin and RBC numbers while a positive significant relationship with WBC and platelet count. Higher concentrations of air pollutants exposed to pregnant women significantly higher the incidence of pregnancy anemia’s. A strong dose-response relationship was confirmed for both contaminants. It is difficult to establish a causal relationship between specific environmental exposures and complicated multifactorial health outcomes, the application of non-targeted metabolite profiling to assess the effect of air pollution on blood metabolite. Therefore in the present study the main emphasis has been made on the effects of various polluted environment contaminants, their biochemical effects on the composition of blood and various associated disease.    

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