Artificial intelligence based approach to forecast PM 2.5 during haze episodes: A case study of Delhi, India

Mishra, Dhirendra; Goyal, P.; Upadhyay, Abhishek

doi:10.1016/j.atmosenv.2014.11.050

Cited by 100 publications

(46 citation statements)

References 14 publications

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“…Lang et al generalized the season autoregressive integrated moving average (SARIMA) model to the small-scale time sequences and used this model to make a short-term prediction of PM 2.5 concentration at 10 stations in Hangzhou [16]. Mishra et al studied the meteorological factors and atmospheric pollutants affecting PM 2.5 in Delingha, and predicted the PM 2.5 concentration under haze conditions in Delingha using a combination of a neural network and fuzzy logic [17]. Konovalov et al predicted future PM 10 values in Europe by combining the deterministic prediction method and the linear regression method [18].…”

Section: Study Of Hazementioning

confidence: 99%

A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

et al. 2019

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Haze is the greatest challenge facing China's sustainable development, and it seriously affects China's economy, society, ecology and human health. Based on the uncertainty and suddenness of haze, this paper proposes a novel linear time-varying grey model (GM)(1,N) based on interval grey number sequences. Because the original GM(1,N) model based on interval grey number sequences has constant parameters, it neglects the dynamic change characteristics of parameters over time. Therefore, this novel linear time-varying GM(1,N) model, based on interval grey number sequences, is established on the basis of the original GM(1,N) model by introducing a linear time polynomial. To verify the validity and practicability of this model, this paper selects the data of PM 10 , SO 2 and NO 2 concentrations in Beijing, China, from 2008 to 2018, to establish a linear time-varying GM(1,3) model based on interval grey number sequences, and the prediction results are compared with the original GM(1,3) model. The result indicates that the prediction effect of the novel model is better than that of the original model. Finally, this model is applied to forecast PM 10 concentration for 2019 to 2021 in Beijing, and the forecast is made to provide a reference for the government to carry out haze control.

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Section: Study Of Hazementioning

confidence: 99%

A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

et al. 2019

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“…Another negative effect of air pollution is the formation of acid rain, which harms trees, soils, rivers, and wildlife. Some of the other environmental effects of air pollution are haze, eutrophication, and global climate change (Mishra et al, 2015). Air pollution can cause long-term and short-term health effects.…”

Section: Introductionmentioning

confidence: 99%

Analysis of ambient air quality using fuzzy air quality index: a case study of Delhi, India

Mishra

Goyal

2015

IJEP

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Ambient air quality is one of the most serious environmental concerns in urban areas around the world, especially in developing countries. In recent years, there has been increasing concern over the adverse effects of air pollution on human health. In the present study, the concentrations of air pollutants and their adverse effect at various locations over Delhi are studied using fuzzy air quality index (FAQI). The 24-hourly average concentrations of main criteria pollutants viz., fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ), carbon monoxide (CO) and ozone (O 3 ) over the years 2008 to 2012 have been used for the analysis at three different locations in Delhi (residential-Sirifort, industrial-DCE and busiest traffic intersection-ITO junction). Further, the weightages of criteria pollutants have been determined using an analytical hierarchical process to access the air quality index. The developed methodologies also have been compared with the conventional approach and validated with different statistical measures. The results clearly indicate that the air quality of Delhi is degraded and lies in the ranges of 'bad' and 'very bad' categories.

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“…2 shows the architecture of Neuro-Fuzzy structure. The same methodology as in Mishra et al (2015), has been adapted in the present study. Further, the same methodology as Mishra and Goyal, (2015a), has been adopted for the other statistical models like MLR and ANN analysis in each season.…”

Section: Neuro-fuzzy Modelling: Combination Of Ann and Fuzzy Logicmentioning

confidence: 99%

Neuro-Fuzzy Approach to Forecast NO2 Pollutants Addressed to Air Quality Dispersion Model over Delhi, India

Mishra

Goyal

2016

Aerosol Air Qual. Res.

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Air pollution forecasting is the most important environmental issue in urban areas as it is useful to assess the effects of air pollutants on human health. It has been observed that the air pollution has been increased above the standard level in the urbanized area of Delhi and will be a major problem in the next few years. Therefore, the main objective of the present study is to develop the model that can forecast daily concentrations of air pollutions in one-day advance. In the present study, the artificial intelligence based Neuro-Fuzzy (NF) model has been proposed for air quality forecasting and the concentration of nitrogen dioxide (NO 2 ) pollutant has been chosen for analysis. The available meteorological variables viz. temperature, pressure, relative humidity, wind speed and direction, visibility and the estimated concentrations through AERMOD. The application of introducing AERMOD aims to improve the forecasting ability of model on the basis the emissions from anthropogenic sources. The training and validation have been made with the eight and two year's available seasonal daily data respectively. The evaluation of the model has been made by comparing its results with observed values as well as other statistical models like MLR and ANN, which reveals that the NF model is performing well and can be used for operational use.

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Artificial intelligence based approach to forecast PM 2.5 during haze episodes: A case study of Delhi, India

Cited by 100 publications

References 14 publications

A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

Analysis of ambient air quality using fuzzy air quality index: a case study of Delhi, India

Neuro-Fuzzy Approach to Forecast NO2 Pollutants Addressed to Air Quality Dispersion Model over Delhi, India

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