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 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 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.

scholarly journals Geomatics Approach for Assessment of respiratory disease Mapping

2014 ◽  
Vol XL-8 ◽  
pp. 205-211 ◽  
Author(s):  
M. Pandey ◽  
V. Singh ◽  
R. C. Vaishya
Keyword(s):  
Fuzzy Logic ◽  
Air Quality ◽  
Human Health ◽  
Quality Index ◽  
Spatial Clustering ◽  
Statistical Significance ◽  
Ambient Air ◽  
Industrial Area ◽  
Air Quality Index ◽  
Statistical Techniques

Air quality is an important subject of relevance in the context of present times because air is the prime resource for sustenance of life especially human health position. Then with the aid of vast sums of data about ambient air quality is generated to know the character of air environment by utilizing technological advancements to know how well or bad the air is. This report supplies a reliable method in assessing the Air Quality Index (AQI) by using fuzzy logic. The fuzzy logic model is designed to predict Air Quality Index (AQI) that report monthly air qualities. With the aid of air quality index we can evaluate the condition of the environment of that area suitability regarding human health position. For appraisal of human health status in industrial area, utilizing information from health survey questionnaire for obtaining a respiratory risk map by applying IDW and Gettis Statistical Techniques. Gettis Statistical Techniques identifies different spatial clustering patterns like hot spots, high risk and cold spots over the entire work area with statistical significance.

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 AMBIENT AIR QUALITY STATUS OF DEWAS INDUSTRIAL AREA OF MADHYA PRADESH, INDIA

2020 ◽  
pp. 4-12
Author(s):  
Reeta Kori ◽  
Alok Saxena ◽  
Harish Wankhade ◽  
Asad Baig ◽  
Ankita Kulshreshtha ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Quality Index ◽  
Ambient Air ◽  
Industrial Area ◽  
Madhya Pradesh ◽  
Air Quality Index ◽  
Particulate Phase ◽  
Ambient Air Quality ◽  
Quality Status

A study has been conducted to assess the ambient air quality status of Dewas industrial area of Madhya Pradesh, India. Total nine locations were selected in Dewas industrial area for ambient air quality monitoring. The eleven pollutants mainly particulate matter less than 10 µ size (PM10), particulate matter less than 2.5 µ size (PM2.5), nitrogen dioxide (NO2), sulphur dioxide (SO2), ozone (O3), ammonia (NH3), benzene (C6H6), benzo (a) Pyrene (BaP) – particulate phase, lead (Pb), Arsenic (As) and Nickel (Ni) were monitored during different four quarters from April 2019 to March 2020. The study revealed that average concentration of gaseous pollutants viz, NO2, SO2, O3, NH3, C6H6 in ambient air were well within standard limits at all selected locations, however concentration of particulate matter (PM10, PM2.5) and heavy metals (Pb & Ni) except As level were found exceeding the National Ambient Air Quality Standards (NAAQS) 2009, India at few monitoring locations. Benzo (a) Pyrene (BaP) –particulate phase in ambient air was not detected during this study. Ambient air Quality Index was found to be moderate (115.56-198.36) at six locations and satisfactory (17.60-94.94) at three locations in Dewas industrial area. Overall ambient Air Quality Index of Dewas industrial area was observed, satisfactory to moderate during this study w.r.t. Air Quality Index. KEY WORDS: Industrial Area, Ambient Air, Air Pollutants, Air Quality Index

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.

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