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

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.

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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.

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Analysis of Air Quality Index Distribution of PM10 and PM2.5 Concentrations in Ambient Air of Al-Hillah City, Iraq

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1058/1/012014 ◽

2021 ◽

Vol 1058 (1) ◽

pp. 012014

Author(s):

Ruqayah Ali Grmasha ◽

Shahla N. A. Al-Azzawi ◽

Osamah J. Al-sareji ◽

Talal Alardhi ◽

Mawada Abdellatif ◽

...

Keyword(s):

Air Quality ◽

Quality Index ◽

Ambient Air ◽

Air Quality Index ◽

Pm10 And Pm2.5 ◽

Index Distribution

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Geomatics Approach for Assessment of respiratory disease Mapping

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-8-205-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 205-211 ◽

Cited By ~ 4

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.

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Emission from Uyo Main Refuse Dumpsite and Potential Impact on Health

International Journal of Environment and Climate Change ◽

10.9734/ijecc/2020/v10i530196 ◽

2020 ◽

pp. 8-13

Author(s):

S. A. Nta ◽

M. J. Ayotamuno ◽

A. H. Igoni ◽

R. N. Okparanma

Keyword(s):

Air Quality ◽

Quality Index ◽

Ambient Air ◽

Air Quality Index ◽

Volatile Organic Carbon ◽

Highly Sensitive ◽

Volatile Organic ◽

Landfill Emissions ◽

Potential Impact ◽

Pm2.5 And Pm10

This paper presents potential impact on health of emission from landfill site on Uyo village road, Uyo local government area of Akwa Ibom State, Nigeria. Three sampling points were assessed for particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2), carbon monoxide (CO), hydrogen sulphide H2S, ammonia (NH3), total volatile organic carbon (TVOC) and hydrogen cyanide (HCN) using highly sensitive digital portable meters. The data obtained were expressed in terms of an air quality index. Air quality index indicates that the ambient air can be described as unhealthy for sensitive groups for NO2, unhealthy for SO2 and PM2.5 and moderate for CO, respectively. H2S, NH3, TVOC, HCN, PM10 were not indicated in USEPA air quality standards. It recommended that stringent and proper landfill emissions management together with appropriate burning of wastes should be considered in the study area to ease the risks associated with these pollutants on public health.

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Ambient Air Quality Monitoring Studies in Four Specific Location of Tamilnadu, India

International Research Journal of Multidisciplinary Technovation ◽

10.34256/irjmtcon96 ◽

2019 ◽

pp. 677-683

Author(s):

Mageshkumar P ◽

Ramesh S ◽

Angu Senthil K

Keyword(s):

Air Quality ◽

Quality Index ◽

High Volume ◽

Ambient Air ◽

Quality Parameters ◽

Air Quality Index ◽

Gaseous Pollutants ◽

Ambient Air Quality ◽

Air Sampler

A comprehensive study on the air quality was carried out in four locations namely, Tiruchengode Bus Stand, K.S.R College Campus, Pallipalayam Bus Stop and Erode Government Hospital to assess the prevailing quality of air. Ambient air sampling was carried out in four locations using a high volume air sampler and the mass concentrations of PM10, PM2.5, SO2, NOX and CO were measured. The analyzed quality parameters were compared with the values suggested by National Ambient Air Quality Standards (NAAQS). Air quality index was also calculated for the gaseous pollutants and for Particulate Matters. It was found that PM10 concentration exceeds the threshold limits in all the measured locations. The higher vehicular density is one of the main reasons for the higher concentrations of these gaseous pollutants. The air quality index results show that the selected locations come under moderate air pollution.

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Temporal Cross-Correlations between Ambient Air Pollutants and Seasonality of Tuberculosis: A Time-Series Analysis

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16091585 ◽

2019 ◽

Vol 16 (9) ◽

pp. 1585 ◽

Cited By ~ 6

Author(s):

Hua Wang ◽

Changwei Tian ◽

Wenming Wang ◽

Xiaoming Luo

Keyword(s):

Time Series ◽

Air Pollutants ◽

Quality Index ◽

Generalized Additive Model ◽

Additive Model ◽

Ambient Air ◽

Delayed Effect ◽

Incidence Data ◽

Ambient Air Pollutants ◽

Cross Correlations

The associations between ambient air pollutants and tuberculosis seasonality are unclear. We assessed the temporal cross-correlations between ambient air pollutants and tuberculosis seasonality. Monthly tuberculosis incidence data and ambient air pollutants (PM2.5, PM10, carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2)) and air quality index (AQI) from 2013 to 2017 in Shanghai were included. A cross-correlogram and generalized additive model were used. A 4-month delayed effect of PM2.5 (0.55), PM10 (0.52), SO2 (0.47), NO2 (0.40), CO (0.39), and AQI (0.45), and a 6-month delayed effect of O3 (−0.38) on the incidence of tuberculosis were found. The number of tuberculosis cases increased by 8%, 4%, 18%, and 14% for a 10 μg/m3 increment in PM2.5, PM10, SO2, and NO2; 4% for a 10 unit increment in AQI; 8% for a 0.1 mg/m3 increment in CO; and decreased by 4% for a 10 μg/m3 increment in O3. PM2.5 concentrations above 50 μg/m3, 70 μg/m3 for PM10, 16 μg/m3 for SO2, 47 μg/m3 for NO2, 0.85 mg/m3 for CO, and 85 for AQI, and O3 concentrations lower than 95 μg/m3 were positively associated with the incidence of tuberculosis. Ambient air pollutants were correlated with tuberculosis seasonality. However, this sort of study cannot prove causality.

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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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Land use regression modelling of ambient air pollutants in Hong Kong

10.5194/egusphere-egu2020-12648 ◽

2020 ◽

Author(s):

Zhiyuan Li ◽

Steve Hung Lam Yim ◽

Kin-Fai Ho

Keyword(s):

Land Use ◽

Hong Kong ◽

Air Quality ◽

Air Pollutants ◽

Ambient Air ◽

Air Pollutant ◽

High Rise ◽

Pollutant Concentrations ◽

Air Quality Measurements ◽

Air Pollutant Concentrations

<p>Land use regression (LUR) models estimate air pollutant concentrations for areas without air quality measurements, which provides valuable information for exposure assessment and epidemiological studies. In the present study, we developed LUR models for ambient air pollutants in Hong Kong, China, a typical high-density and high-rise city. Air quality measurements at sixteen air quality monitoring stations, operated by the Hong Kong Environmental Protection Department, were collected. Moreover, five categories of predictor variables, including population distribution, traffic emissions, land use variables, urban/building morphology, and meteorological parameters, were employed to establish the LUR models of various air pollutants. Then the spatial distribution of air pollutant concentrations at 1 km &#215; 1 km grid cells were plotted. Taking fine particle (PM2.5) as an example, the developed LUR model explained 89% of variability of PM2.5 concentrations, with a leave-one-out-cross-validation R2 of 0.64. LUR modelling results for other air pollutants will be presented. In addition, further improvements on the development of LUR models will be discussed. This study can help to assess long-term exposures to air pollutants for high-density and high-rise urban areas like Hong Kong.</p>

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