Inferring air pollution from air quality index by different geographical areas: case study in India

Sidoarjo mudflow or known as Lapindo mudflow erupted since 2006. The Sidoarjo mudflow is located in Sidoarjo City, East Java, Indonesia. The mudflow-affected area has high air pollution level and high health risk. Therefore, in this paper was implemented a system that can categorize the level of air pollution into several categories. The air quality index can be categorized using fuzzy logic algorithm based on the concentration of air pollutant parameters in the mudflow-affected area. Furthermore, Dataflow programming is used to process the fuzzy logic algorithm. Based on the result, the measurement accuracy of the air quality index in the mudflow-affected area has an accuracy rate of 93.92% in Siring Barat, 93.34% in Mindi, and 95.96% in Jatirejo. The methane concentration is passes the standard quality even though the air quality index is safe. Hence, the area is indicated into Hazardous level. In addition, Mindi has highest and stable methane concentration. It means that Mindi has high-risk air pollution.

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

10.21203/rs.3.rs-202216/v1 ◽

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.

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Real Time Air Quality Index from Various Locations

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b3493.079220 ◽

2020 ◽

Vol 9 (2) ◽

pp. 368-372

Keyword(s):

Air Pollution ◽

Air Quality ◽

Real Time ◽

Environmental Protection Agency ◽

Quality Index ◽

Ground Level ◽

Air Quality Index ◽

Time Data ◽

Open Government Data ◽

Government Data

The surveys regarding air pollution shows that there has been a hasty growth due to the emission of fuels and exhaust gases from factories. The Air Quality Index (AQI) has been launched to note the contemporary status of the air quality. The intent of AQI is to aid every individual know how the regional air quality will make an impact on them. The Environmental Protection Agency assess the AQI for five major air pollutants namely Nitrogen dioxide (NO2), ground-level ozone (O3), particle pollution (PM10, PM2.5), carbon monoxide (CO), and sulphur dioxide (SO2). The intent of the project is to congregate real-time Air Quality Index from distinct monitoring stations across India, analysing the data and reporting on it. Collect the real-time data using the API key provided by Open Government Data (OGD) platform India. This is done by making use of Microsoft Business Intelligence (MSBI) and Power BI Tools to transform, analyse and visualize the data. This project can be utilized to develop various programs like Ozone today in Europe and in mobile applications which acts as an alert system that can protect people from air pollution.

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Air Pollution and Nasal Mucociliary Clearance Time among Urban and Rural Residents in Two Philippine Communities

Philippine Journal of Otolaryngology Head and Neck Surgery ◽

10.32412/pjohns.v35i1.1275 ◽

2020 ◽

Vol 35 (1) ◽

pp. 33-35

Author(s):

Soraya Joson ◽

Joman Laxamana

Keyword(s):

Air Pollution ◽

Methylene Blue ◽

Air Quality ◽

Quality Index ◽

Mucociliary Clearance ◽

Air Quality Index ◽

Clearance Time ◽

Rural Residents ◽

Air Index ◽

Nasal Mucociliary Clearance

ABSTRACT Objective: To measure the nasal mucociliary clearance (NMC) time among adults residing in two Philippine communities with different air quality indices using the saccharin and methylene blue test. Methods: Design: Cross-Sectional Study Setting: Diliman, Quezon City and Puerto Princesa, Palawan Participantss: Fifty (50) participants, 25 residing in an urban city with fair air quality index and 25 residing in a rural province with good air quality index. Results: The mean NMC time of the urban group was 22.15±12.68 mins and was significantly longer than the NMC time of the rural group which was 5.29±2.87mins; t(48) = 6.643, p<0.0001). Conclusion: Increased air pollution may be associated with significant prolongation of nasal mucociliary clearance time among urban residents with fair quality air index compared to rural residents with good quality air index. Keywords: nasal mucociliary clearance, naso mucociliary clearance time, air pollution, air quality index, saccharin test, methylene blue

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Spatial air quality index prediction model based on decomposition, adaptive boosting, and three-stage feature selection: A case study in China

Journal of Cleaner Production ◽

10.1016/j.jclepro.2020.121777 ◽

2020 ◽

Vol 265 ◽

pp. 121777 ◽

Cited By ~ 2

Author(s):

Hui Liu ◽

Chao Chen

Keyword(s):

Feature Selection ◽

Air Quality ◽

Prediction Model ◽

Quality Index ◽

Air Quality Index ◽

Adaptive Boosting ◽

Model Based

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Endogenous or Exogenous? Examining Trans-Boundary Air Pollution by Using the Air Quality Index (AQI): A Case Study of 30 Provinces and Autonomous Regions in China

Sustainability ◽

10.3390/su10114220 ◽

2018 ◽

Vol 10 (11) ◽

pp. 4220 ◽

Cited By ~ 8

Author(s):

Wenyang Huang ◽

Huiwen Wang ◽

Yigang Wei

Keyword(s):

Air Pollution ◽

Air Quality ◽

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Quality Index ◽

Air Quality Index ◽

Oil Consumption ◽

Sar Model ◽

Autonomous Regions

China is experiencing severe environmental degradation, particularly air pollution. To explore whether air pollutants are spatially correlated (i.e., trans-boundary effects) and to analyse the main contributing factors, this research investigates the annual concentration of the Air Quality Index (AQI) and 13 polluting sectors in 30 provinces and autonomous regions across China. Factor analysis, the linear regression model and the spatial auto-regression (SAR) model are employed to analyse the latest data in 2014. Several important findings are derived. Firstly, the global Moran’s I test reveals that the AQI of China shows a distinct positive spatial correlation. The local Moran’s I test shows that significant high–high AQI agglomeration regions are found around the Beijing–Tianjin–Hebei area and the regions of low–low AQI agglomeration all locate in south China, including Yunnan, Guangxi and Fujian. Secondly, the effectiveness of the SAR model is much better than that of the linear regression model, with a significantly improved R-squared value from 0.287 to 0.705. A given region’s AQI will rise by 0.793% if the AQI of its ambient region increases by 1%. Thirdly, car ownership, steel output, coke output, coal consumption, built-up area, diesel consumption and electric power output contribute most to air pollution according to AQI, whereas fuel oil consumption, caustic soda output and crude oil consumption are inconsiderably accountable in raising AQI. Fourthly, the air quality in Beijing and Tianjin is under great exogenous influence from nearby regions, such as Hebei’s air pollution, and cross-boundary and joint efforts must be committed by the Beijing–Tianjin–Hebei region in order to control air pollution.

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A psychophysical measurement on subjective well-being and air pollution

Nature Communications ◽

10.1038/s41467-019-13459-w ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 4

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.

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

Applied Sciences ◽

10.3390/app9194069 ◽

2019 ◽

Vol 9 (19) ◽

pp. 4069 ◽

Cited By ~ 2

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.

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