scholarly journals A Literature Review on Prediction of Air Quality Index and Forecasting Ambient Air Pollutants using Machine Learning Algorithms

2020 ◽  
Vol 5 (8) ◽  
pp. 1148-1152
Author(s):  
Radhika M. Patil ◽  
Dr. H. T. Dinde ◽  
Sonali. K. Powar
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Literature Review ◽  
Air Pollutants ◽  
Quality Index ◽  
Ambient Air ◽  
Air Quality Index ◽  
Machine Learning Algorithms ◽  
Ambient Air Pollutants

Day by day the air pollution becomes serious concern in India as well as in overall world. Proper or accurate prediction or forecast of Air Quality or the concentration level of other Ambient air pollutants such as Sulfur Dioxide, Nitrogen Dioxide, Carbon Monoxide, Particulate Matter having diameter less than 10µ, Particulate Matter having diameter less than 2.5µ, Ozone, etc. is very important because impact of these factors on human health becomes severe. This literature review focuses on the various techniques used for prediction or modelling of Air Quality Index (AQI) and forecasting of future concentration levels of pollutants that may cause the air pollution so that governing bodies can take the actions to reduce the pollution.

Application of nonlinear land use regression models for ambient air pollutants and air quality index

2021 ◽  
Vol 12 (10) ◽  
pp. 101186
Author(s):  
Licheng Zhang ◽  
Xue Tian ◽  
Yuhan Zhao ◽  
Lulu Liu ◽  
Zhiwei Li ◽  
...  
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Air Pollutants ◽  
Regression Models ◽  
Quality Index ◽  
Ambient Air ◽  
Air Quality Index ◽  
Land Use Regression ◽  
Ambient Air Pollutants

scholarly journals Air Quality Index and Air Pollutant Concentration Prediction Based on Machine Learning Algorithms

2019 ◽  
Vol 9 (19) ◽  
pp. 4069 ◽  
Author(s):  
Huixiang Liu ◽  
Qing Li ◽  
Dongbing Yu ◽  
Yu Gu
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Regression Models ◽  
Quality Index ◽  
Air Pollutant ◽  
Air Quality Index ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Support Vector

Air pollution has become an important environmental issue in recent decades. Forecasts of air quality play an important role in warning people about and controlling air pollution. We used support vector regression (SVR) and random forest regression (RFR) to build regression models for predicting the Air Quality Index (AQI) in Beijing and the nitrogen oxides (NOX) concentration in an Italian city, based on two publicly available datasets. The root-mean-square error (RMSE), correlation coefficient (r), and coefficient of determination (R2) were used to evaluate the performance of the regression models. Experimental results showed that the SVR-based model performed better in the prediction of the AQI (RMSE = 7.666, R2 = 0.9776, and r = 0.9887), and the RFR-based model performed better in the prediction of the NOX concentration (RMSE = 83.6716, R2 = 0.8401, and r = 0.9180). This work also illustrates that combining machine learning with air quality prediction is an efficient and convenient way to solve some related environment problems.

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 Understanding the Spatial-Temporal Patterns and Influential Factors on Air Quality Index: The Case of North China

2019 ◽  
Vol 16 (16) ◽  
pp. 2820 ◽  
Author(s):  
Wenxuan Xu ◽  
Yongzhong Tian ◽  
Yongxue Liu ◽  
Bingxue Zhao ◽  
Yongchao Liu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Wind Speed ◽  
Quality Index ◽  
North China ◽  
Ambient Air ◽  
Air Quality Index ◽  
Rapid Expansion ◽  
Spatial Data Analysis ◽  
Prevailing Wind Direction

North China has become one of the worst air quality regions in China and the world. Based on the daily air quality index (AQI) monitoring data in 96 cities from 2014–2016, the spatiotemporal patterns of AQI in North China were investigated, then the influence of meteorological and socio-economic factors on AQI was discussed by statistical analysis and ESDA-GWR (exploratory spatial data analysis-geographically weighted regression) model. The principal results are as follows: (1) The average annual AQI from 2014–2016 exceeded or were close to the Grade II standard of Chinese Ambient Air Quality (CAAQ), although the area experiencing heavy pollution decreased. Meanwhile, the positive spatial autocorrelation of AQI was enhanced in the sample period. (2) The occurrence of a distinct seasonal cycle in air pollution which exhibit a sinusoidal pattern of fluctuations and can be described as “heavy winter and light summer.” Although the AQI generally decreased in other seasons, the air pollution intensity increased in winter with the rapid expansion of higher AQI value in the southern of Hebei and Shanxi. (3) The correlation analysis of daily meteorological factors and AQI shows that air quality can be significantly improved when daily precipitation exceeds 10 mm. In addition, except for O3, wind speed has a negative correlation with AQI and major pollutants, which was most significant in winter. Meanwhile, pollutants are transmitted dynamically under the influence of the prevailing wind direction, which can result in the relocation of AQI. (4) According to ESDA-GWR analysis, on an annual scale, car ownership and industrial production are positively correlated with air pollution; whereas increase of wind speed, per capita gross domestic product (GDP), and forest coverage are conducive to reducing pollution. Local coefficients show spatial differences in the effects of different factors on the AQI. Empirical results of this study are helpful for the government departments to formulate regionally differentiated governance policies regarding air pollution.

APPLICATION OF AIR QUALITY INDEX AND INVERSE DISTANCE WEIGHTING FOR MAPPING THE DISTRIBUTION OF AIR POLLUTION AT SEVERAL URBAN DISTRICTS OF HANOI CITY

2020 ◽  
Vol 65 (10) ◽  
pp. 189-200
Author(s):  
Khac Dang Vu ◽  
Anh Nguyen Thi Van
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Ambient Air ◽  
Inverse Distance Weighting ◽  
Air Quality Index ◽  
Pollution Level ◽  
Urban Districts ◽  
Distance Weighting ◽  
Inverse Distance

The air pollution level can be assessed using air quality index - AQI calculated from the concentration of some gases and particle matters which are measured at ambient air quality monitoring stations. The calculated AQI values are characterized by temporal continuity but spatial discontinuity. However, AQI values of each monitoring station is interpolated by the IDW (Inverse Distance Weighting) method in GIS which helps us to assess the air quality at a detailed and specific level for every location in the study area by establishing distribution maps of air pollution. The interpolation of AQI values for zoning air quality in several urban districts of Hanoi during the Winter (October, November, December 2019) shows that in general, the areas with a very bad level of air quality occupied an important surface in the Northwest of urban districts (on the territory of Bac Tu Liem, Ba Dinh, Tay Ho, Cau Giay) for last 3 months of the year. The areas with a bad level of air quality occupied a large surface in the Southeast in October and December, but its surface became narrow in November. But in November, areas having a bad level of air quality were expanded to the Southeast while they occupied only a small surface at the center of the study area in October and December. Although the distribution of each level vary in terms of coverage, their common pattern has been conserved during three months of Winter. The distribution map of air quality provides the complete picture of the air pollution situation and it helps to adequately evaluate this issue in the urban districts of Hanoi city.

scholarly journals Analysis of existing air monitoring technologies

2021 ◽  
Vol 3 (134) ◽  
pp. 67-78
Author(s):  
Volodymyr Tarasov ◽  
Bohdan Molodets ◽  
Тatyana Bulanaya ◽  
Oleg Baybuz
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Ambient Air ◽  
Human Condition ◽  
Air Quality Index ◽  
Air Monitoring ◽  
Atmospheric Air ◽  
Monitoring Technologies ◽  
The Impact

Atmospheric air monitoring is a systematic, long-term assessment of the level of certain types of pollutants by measuring their amount in the open air. Atmospheric air monitoring is an integral part of an effective air quality management system and is carried out through environmental monitoring networks, which should support timely provision of public information about air pollution, support compliance with ambient air quality standards and development of emission strategies, support for air pollution research.The work is devoted to existing air monitoring technologies: ground (sensors, diffusion tubes, etc.) and remote resources (satellites, aircraft, etc.). In addition, standards of air quality assessment (European and American) are described. As an example, we consider the European Air Quality Index (EAQI) and the Air Quality Index according to EPF standards: indicators by which these indices are calculated, the ranking of air status depending on the value of the index are described.AQI (Air Quality Index) is used as an indicator of the impact of air on the human condition. The European Air Quality Index allows users to better understand air quality where they live, work or travel. By displaying information for Europe, users can gain an understanding of air quality in individual countries, regions and cities. The index is based on the values of the concentration of the five main pollutants, including particles less than 10μm (PM10), particles less than 2.5μm (PM2.5), ozone (O3); nitrogen dioxide (NO2); sulfur dioxide (SO2). To conclude, ground stations give a more accurate picture of the state of the air at a point, while satellite image data with a certain error (due to cloud cover, etc.) can cover a larger area and solve the problem of coverage of stations in the area. There is no single standard for calculation. Today, the European Air Quality Index (EAQI) is used in Ukraine and Europe.

scholarly journals Trend Analysis of Air Quality Index (AQI) and Greenhouse Gas (GHG) Emissions in Taiwan and Their Regulatory Countermeasures

Environments ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 29
Author(s):  
Wen-Tien Tsai ◽  
Yu-Quan Lin
Keyword(s):  
Climate Change ◽  
Air Pollution ◽  
Air Quality ◽  
Greenhouse Gas ◽  
Air Pollutants ◽  
Quality Index ◽  
Ghg Emissions ◽  
Air Quality Index ◽  
Air Pollution Control ◽  
Criteria Air Pollutants

A reduction in the energy-related emissions of air pollutants would not only mitigate climate change but would also improve local air quality and public health. This paper aimed to analyze the trends of air quality index (AQI) and greenhouse gas (GHG) emissions in Taiwan by using the latest official statistics. In addition, this study also summarized regulatory measures for controlling air pollution from the energy sector with relevance to sustainable development goals (SDGs). With the joint efforts by the public and private sectors, the change in the total GHG emissions did not vary much with the exception of 2009, ranging from 250 to 272 million metric tons of CO2 equivalent from 2005 through 2019. Based on the data on AQI, the percentage of AQI by station-day with AQI > 100 has decreased from 18.1% in 2017 to 10.1% in 2020, indicating a decreasing trend for all criteria air pollutants. On the other hand, the coronavirus disease (COVID-19) lockdown, in 2019, has positively impacted Taiwan’s urban air quality, which was consistent with those observed in other countries. This consistent situation could be attributed to the climate change mitigation policies and promotional actions under the revised Air Pollution Control Act and the Greenhouse Gas Reduction and Management Act of 2015. In response to the SDGslaunched by the Taiwan government in 2018, achieving the relevant targets by 2030 can be prospective.

scholarly journals Ambient Air Quality Assessment Using Air Quality Index of Delhi

10.29007/mpmq ◽  
2018 ◽  
Author(s):  
Jaykumar Patel ◽  
Hirva Salvi ◽  
Neha Patel
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Quality Index ◽  
Monsoon Season ◽  
Urban Air Pollution ◽  
Winter Season ◽  
Ambient Air ◽  
Data Interpretation ◽  
Air Quality Index ◽  
Central Pollution Control Board

Urban air pollution is rapidly increasing in Indian cities. It affects the health and mental status of urban dwellers. In the present study, air pollutants data were collected for a year 2016 at 4 locations in Delhi from Central Pollution Control Board. The present study incorporates the analysis of the ambient air in Delhi city using Air Quality Index (AQI). An AQI is proposed for the city of Delhi, India for easy data interpretation and understanding of air quality. The air pollutants analyzed are Sulfur dioxide (SO2), Nitrogen dioxide (NO2) and Particulate matter (PM2.5). The locations selected are Dwarka, R.K Puram, Panjabi Baugh, and Anand Vihar. The AQI were calculated using IND-AQI procedure. It has been observed that AQI’s values of all four locations falls under very poor category. The overall AQI was found under very poor and sever categories. It was found that AQI values were very high during winter season and low during monsoon season. The AQI of PM2.5 was found exceeding the limits for all the months in each location. Thus, it is observed that PM2.5 is critical pollutant at these four locations in Delhi.

scholarly journals A comparative assessment of air pollutants of smog in wagah border and other sites in Lahore, Pakistan

2024 ◽  
Vol 84 ◽  
Author(s):  
H. S. Yousaf ◽  
M. Abbas ◽  
N. Ghani ◽  
H. Chaudhary ◽  
A. Fatima ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Air Pollutants ◽  
Quality Index ◽  
Pearson Correlation ◽  
Air Quality Index ◽  
Pm 2.5 ◽  
Secondary Air Pollutants ◽  
Secondary Air ◽  
Pm 10

Abstract Smog has become the fifth season of Pakistan especially in Lahore city. Increased level of air pollutants (primary and secondary) are thought to be responsible for the formation of smog in Lahore. Therefore, the current study was carried out for the evaluation of air pollutants (primary and secondary) of smog in Wagah border particularly and other sites (Jail road, Gulburg) Lahore. For this purpose, baseline data on winter smog from March to December on primary and secondary air pollutants and meteorological parameters was collected from Environmental Protection Department and Pakistan Meteorological Department respectively. Devices being used in both departments for analysis of parameters were also studied. Collected data was further statistically analyzed to determine the correlation of parameters with meteorological conditions and was subjected to air quality index. According to results, PM 10 and PM 2.5 were found very high above the NEQS. NOx concentrations were also high above the permissible limits whereas SO2 and O3 were found below the NEQS thus have no roles in smog formation. Air Quality Index (AQI) of pollutants was PM 2.5(86-227), PM 10 (46-332), NOx (26-110), O3 (19-84) and SO2 (10-95). AQI of PM 2.5 remained between moderate to very unhealthy levels. AQI of PM 10 remained between good to hazardous levels. AQI of NOx remained between good to unhealthy for sensitive groups’ levels. AQI of O3 and SO2 remained between good to moderate levels. Pearson correlation showed that every pollutant has a different relation with different or same parameters in different areas. It is concluded from the present study that particulate matter was much more responsible for smog formation. Although NOx also played role in smog formation. So there is need to reduce sources of particulate matter and NOx specifically in order to reduce smog formation in Lahore.

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