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Proceedings ArticleDOI

IoT System based Forecasting and Modeling Exceedance Probability and Return Period of Air Quality using Extreme Value Distribution

11 Mar 2019-pp 1-6
TL;DR: The results indicate that the EVD based air quality model is best suited for forecasting air quality in this high pollutant concentration region.
Abstract: This work aims to collect and analyze air quality information in and around regions of Delhi using an IoT system which is placed in a moving vehicle. Hourly air quality data has been used to forecast exceedance probabilities and return periods for air pollution extremes. The exceedance probabilities and return periods obtained using Extreme Value Distribution (EVD) have been compared to actual occurrences of extreme events.The results indicate that the EVD based air quality model is best suited for forecasting air quality in this high pollutant concentration region.
Citations
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Journal ArticleDOI
TL;DR: An IoT-based automated framework for monitoring and predicting air quality parameters like benzene using machine learning technology and an Adaptive Neuro-Fuzzy Inference System technique to predict the air quality in the form of Level of Pollutant (LoP) and modified Air Quality Index (m-AQI).
Abstract: The drastic increase in atmospheric pollutants has resulted in the prevalence of hazardous diseases like Asthma, Ischaemic heart disease, and Pulmonary disease around the world. IoT technology has ...

5 citations


Cites background from "IoT System based Forecasting and Mo..."

  • ...Barthwal et al. (2019) collected and analysed air quality information around JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 427...

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  • ...Barthwal et al. (2019) collected and analysed air quality information around the regions of New Delhi, India by utilising IoT technology....

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Proceedings ArticleDOI
08 Jul 2022
TL;DR: In this paper , the authors have collected real-time AQI data at the hotspot and its neighborhoods on a specific route over a period and tried to develop a mathematical model which forecasts the variation of AQI with distance.
Abstract: Most Indian cities have seen rapid urbanization due to huge migration of population leading to a substantial rise in construction activities, vehicular emissions, and uncontrolled growth. Some such cities also house many pollutions causing industries that result in deterioration of air quality. These cities have pollution hotspots where pollution levels are much higher than permitted limits. Air pollution is highly location-centric and varies greatly on moving away from the hotspots. Because these Air Quality Index(AQI) data are typically unavailable, the long-term impact of these hotspots on adjacent neighborhoods is unknown. If the fluctuation in pollution in adjacent neighborhoods as we move away from hotspots can be modeled and projected, this information will be extremely beneficial for the government, and city administrations in better planning development activities as well as issuing suitable recommendations to sensitive establishments such as educational institutes, hospitals, and old age homes, among others. In this work, we have collected the real-time AQI data at the hotspot and its neighborhoods on a specific route over a period and tried to develop a mathematical model which forecasts the variation of AQI with distance.
Book ChapterDOI
20 Feb 2020
TL;DR: In this paper, a bootstrap scheme is proposed to obtain the distribution of the considered air pollutant at a given time point, based on the residuals of a semiparametric model which is able to incorporate some stylized facts usually observed in such kind of data, such as missing data, trends and conditional heteroscedasticity.
Abstract: In the last years, increasing attention has been paid to air pollution, due to its impact on human health and on the environment. Current EU legislation establishes fixed limits for some air components that have been shown to have adverse effects on human health. It is therefore important to identify regions where the probability of exceeding those limits is high. In this paper, we propose a bootstrap scheme to obtain the distribution of the considered air pollutant at a given time point. In particular, the proposed resampling scheme is based on the residuals of a semiparametric model which is able to incorporate some stylized facts usually observed in such kind of data, such as missing data, trends and conditional heteroscedasticity. The estimated bootstrap distribution is then used to estimate the probability that the air pollutant exceeds the fixed legal limits. An application to (\(PM_{10}\)) in Torino area in the North-Italian region Piemonte is shown.
Journal ArticleDOI
TL;DR: In this paper , a mathematical model was developed to forecast how AQI varies with distance for best results, using various forecasting error calculation methods such as MPE (mean percentage error), MAP (Mean absolute percentage), MAD (means absolute deviation), RMSE (Root Mean Square Error), and MSE (Means Square Error).
Abstract: Due to massive population migration, most Indian cities have experienced fast urbanization, resulting in a significant increase in construction activity, traffic pollution, and uncontrolled expansion. Some of these cities also have a high concentration of polluting industries, significantly worsening air quality. Pollution hotspots exist in certain cities, with levels well surpassing the authorized mark. Air pollution is generally classified as extremely hyper-local, which signifies that the pollution index decreases as we travel away from hotspots. Since the pollution data collected from traditional sources is occasionally inadequate, the extended consequences of such hotspots on neighboring communities remain unidentified. If the flux in pollution values in neighboring locales is efficiently mapped for locations encountered travelling further from identified hotspots, AQI levels for these areas can be forecasted and projected. Knowledge from monitoring these levels will aid the city administrations and government in drafting suitable proposals for susceptible establishments like hospitals and schools. In this research work, the Air Quality Index (AQI) data was accurately gathered at an identified pollution hotspot and its immediate neighborhood over a defined period along a specific route and a mathematical model was developed to forecast how AQI varies with distance for best results. Stochastic models such as ARMA and ARIMA were used to create the predicted model. Its reliability and performance were measured using various forecasting error calculation methods such as MPE (Mean Percentage Error), MAP (Mean Absolute Percentage), MAD (Mean Absolute Deviation), RMSE (Root Mean Square Error), and MSE (Mean Square Error).
Proceedings ArticleDOI
08 Jul 2022
TL;DR: In this paper , the authors have collected the real-time AQI data at the hotspot and its neighborhoods on a specific route over a period and tried to develop a mathematical model which forecasts the variation of AQI with distance.
Abstract: Most Indian cities have seen rapid urbanization due to huge migration of population leading to a substantial rise in construction activities, vehicular emissions, and uncontrolled growth. Some such cities also house many pollutions causing industries that result in deterioration of air quality. These cities have pollution hotspots where pollution levels are much higher than permitted limits. Air pollution is highly location-centric and varies greatly on moving away from the hotspots. Because these Air Quality Index(AQI) data are typically unavailable, the long-term impact of these hotspots on adjacent neighborhoods is unknown. If the fluctuation in pollution in adjacent neighborhoods as we move away from hotspots can be modeled and projected, this information will be extremely beneficial for the government, and city administrations in better planning development activities as well as issuing suitable recommendations to sensitive establishments such as educational institutes, hospitals, and old age homes, among others. In this work, we have collected the real-time AQI data at the hotspot and its neighborhoods on a specific route over a period and tried to develop a mathematical model which forecasts the variation of AQI with distance.
References
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Journal ArticleDOI
TL;DR: In this paper, the authors explored spatial and temporal trends in mortality and burden of disease attributable to ambient air pollution from 1990 to 2015 at global, regional, and country levels, and estimated the relative risk of mortality from ischaemic heart disease, cerebrovascular disease, chronic obstructive pulmonary disease, lung cancer, and lower respiratory infections from epidemiological studies using nonlinear exposure-response functions spanning the global range of exposure.

3,960 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a system analytical perspective on management options that could efficiently improve air quality at the urban scale, having Delhi as a case study, and employ the newly developed GAINS-City policy analysis framework, consisting of a bottom up emission calculation combined with atmospheric chemistry-transport calculation, to derive innovative insights into the current sources of pollution and their impacts on ambient PM2.5, both from emissions of primary PM as well as precursors of secondary inorganic and organic aerosols.

70 citations


"IoT System based Forecasting and Mo..." refers background in this paper

  • ...Quality of air in the Delhi and NCR has worsened due to vehicular emissions, exhaust from diesel generators, dust from construction sites, burning garbage and agricultural waste, thermal power plants and industrial activities [3][4]....

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Journal ArticleDOI
TL;DR: The ambient pollutant concentrations in Qingdao in winter could be attributed to local primary emissions, coal combustion, vehicular, domestic and industrial emissions, secondary formation, and long distance transmission of emissions.

57 citations


"IoT System based Forecasting and Mo..." refers background in this paper

  • ...Authors in [14] analyzed hourly data of PM2....

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Journal ArticleDOI
TL;DR: In this article, the authors compared the air quality between the two largest Brazilian urban areas and provide information for decision makers, government agencies and civil society by applying generalized extreme value (GEV) and generalized pareto distribution (GPD) to investigate the behavior of pollutants.
Abstract: Sixteen years of hourly atmospheric pollutant data (1996–2011) in the Metropolitan Area of Sao Paulo (MASP), and seven years (2005–2011) of data measured in the Metropolitan Area of Rio de Janeiro (MARJ), were analyzed in order to study the extreme pollution events and their return period. In addition, the objective was to compare the air quality between the two largest Brazilian urban areas and provide information for decision makers, government agencies and civil society. Generalized Extreme Value (GEV) and Generalized Pareto Distribution (GPD) were applied to investigate the behavior of pollutants in these two regions. Although GEV and GPD are different approaches, they presented similar results. The probability of higher concentrations for CO, NO, NO2, PM10 and PM2.5 was more frequent during the winter, and O3 episodes occur most frequently during summer in the MASP. On the other hand, there is no seasonally defined behavior in MARJ for pollutants, with O3 presenting the shortest return period for high concentrations. In general, Ibirapuera and Campos Elisios stations present the highest probabilities of extreme events with high concentrations in MASP and MARJ, respectively. When the regions are compared, MASP presented higher probabilities of extreme events for all analyzed pollutants, except for NO; while O3 and PM2.5 are those with most frequent probabilities of presenting extreme episodes, in comparison other pollutants.

38 citations


"IoT System based Forecasting and Mo..." refers background in this paper

  • ...EVD has been extensively used worldwide to forecast the occurrence of floods, storms, sea waves, droughts, wind, earthquakes, huge fluctuations in exchange rates and market crashes [5] [6] [7]....

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Proceedings ArticleDOI
11 May 2017
TL;DR: The proposed system Monitor the pollution and noise created by vehicle and if any vehicle crosses its threshold value then it will get reported to the traffic department and agencies of national environment.
Abstract: According to population Reference Bureau, the current world population is 7.4 Billion. At present, a total number of the vehicle is 1.2 Billion according to survey and it will be 2 Billion up to 2035. Transportation contributed more than half nitrogen oxide and carbon monoxide and a quarter of the hydrocarbon emitted in air. Cars and trucks turn out pollution throughout their life, as well as pollution get emit throughout the vehicle operation, refueling, producing, and disposal. Due to heavier traffic and powerful engines, noise level in cities is rapidly increasing. Our proposed system provides the solution to these problems with the help of internet of things. Our system Monitor the pollution and noise created by vehicle and if any vehicle crosses its threshold value then it will get reported to the traffic department and agencies of national environment.

25 citations


"IoT System based Forecasting and Mo..." refers background in this paper

  • ...The authors in [11] have developed an IoT based pollution sensing system to monitor the pollution and noise created This full text paper was peer-reviewed at the direction of IEEE Instrumentation and Measurement Society prior to the acceptance and publication....

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