Abstract
The growing of industries and urban areas has induced air pollution that in turn led to several impacts in humans and environment over these years globally. Vast proportion of fine particulate matter (PM) along with its variants, namely, PM2.5, PM 10 and gasoline pollutants (i.e. NOX, SO2, CO) is associated with lung cancer, cardiovascular disease, respiratory and metabolic disorders. Mainly, the air pollution is determined by both PM and gasoline pollutants. However, the meteorological parameters like temperature, humidity and wind have influenced the PM and gasoline pollutants. Prediction of air quality index (AQI) based on the metrological parameters is a complex task. Although many attempts are made to predict the AQI, the meteorological influenced forecast have not been probed further. In this work, a multivariant regressive function is developed as multiple linear regressive (MLR) model to predict AQI based on the correlation of two time series–dependent variables for air pollutants and meteorological parameters. The MLR model gives higher efficacy in AQI prediction while comparing the existing algorithms. Predicted AQI is hosted in social network for welfare of the nation and to initiate an awareness in people about the degradation of air quality and its associated health issues.
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References
Bui X-N et al (2019) Estimating PM10 concentration from drilling operations in open-pit mines using an assembly of SVR and PSO. Appl Sci 9(14):2806. https://doi.org/10.3390/app9142806
Caselli M, Trizio L, de Gennaro G, Ielpo P (2009) A simple feedforward neural network for the PM10 forecasting: comparison with a radial basis function network and a multivariate linear regression model. Water Air Soil Pollut 201(1–4):365–377. https://doi.org/10.1007/s11270-008-9950-2
Javanroodi K, Nik VM (2020) Interactions between extreme climate and urban morphology: investigating the evolution of extreme wind speeds from mesoscale to microscale. Urban Clim 31:100544. https://doi.org/10.1016/j.uclim.2019.100544
Kayes I, Shahriar SA, Hasan K, Akhter M, Kabir MM, Salam MA (2019) The relationships between meteorological parameters and air pollutants in an urban environment. Glob J Environ Sci Manag 5(3):265–278. https://doi.org/10.22034/GJESM.2019.03.01
Khayatian F, Sarto L, Dall’O’ G (2016) Application of neural networks for evaluating energy performance certificates of residential buildings. Energy Build. 125:45–54. https://doi.org/10.1016/j.enbuild.2016.04.067
Ku Yusof KMK et al (2019) The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: a decade case study. Malays J Fundam Appl Sci 15(2):164–172. https://doi.org/10.11113/mjfas.v15n2.1004
Kumar S, Chong I (2018) Correlation analysis to identify the effective data in machine learning: prediction of depressive disorder and emotion states. Int J Environ Res Public Health 15(12):2907. https://doi.org/10.3390/ijerph15122907
Lanzafame R, Monforte P, Patanè G, Strano S (2015) Trend analysis of air quality index in Catania from 2010 to 2014. Energy Procedia 82:708–715. https://doi.org/10.1016/j.egypro.2015.11.796
Li J, Carlson BE, Lacis AA (2015) How well do satellite AOD observations represent the spatial and temporal variability of PM 2.5 concentration for the United States? Atmos Environ 102:260–273. https://doi.org/10.1016/j.atmosenv.2014.12.010
Li Z, Kang Y, Lv W, Wu Y, Chen C, Xu Z (2021) High-emitter identification model establishment using weighted extreme learning machine and active sampling. Neurocomputing 441:79–91. https://doi.org/10.1016/j.neucom.2021.01.074
Liu H, Li Q, Yu D, Gu Y (2019) Air quality index and air pollutant concentration prediction based on machine learning algorithms. Appl Sci 9(19):4069. https://doi.org/10.3390/app9194069
Mahajan S, Kumar P (2020) Evaluation of low-cost sensors for quantitative personal exposure monitoring. Sustain Cities Soc 57:102076. https://doi.org/10.1016/j.scs.2020.102076
Manisalidis I, Stavropoulou E, Stavropoulos A, Bezirtzoglou E (2020) Environmental and health impacts of air pollution: a review. Front Public Health 8:14. https://doi.org/10.3389/fpubh.2020.00014
Miller L, Xu X (2018) Ambient PM2.5 human health effects—findings in China and research directions. Atmosphere 9(11):424. https://doi.org/10.3390/atmos9110424
Motesaddi S, Hashempour Y, Nowrouz P (2017) Characterizing of air pollution in Tehran: comparison of two air quality indices. Civ Eng J 3(9):749–758. https://doi.org/10.21859/cej-030911
Noel C, Vanroelen C, Gadeyne S (2021) Qualitative research about public health risk perceptions on ambient air pollution. A review study. SSM - Popul Health 15:100879. https://doi.org/10.1016/j.ssmph.2021.100879
Pandya S et al (2020) Pollution weather prediction system: smart outdoor pollution monitoring and prediction for healthy breathing and living. Sensors 20(18):5448. https://doi.org/10.3390/s20185448
Sharma E, Deo RC, Prasad R, Parisi AV (2020) A hybrid air quality early-warning framework: an hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms. Sci Total Environ 709:135934. https://doi.org/10.1016/j.scitotenv.2019.135934
Shih D-H, Wu T-W, Liu W-X, Shih P-Y (2019) An Azure ACES early warning system for air quality index deteriorating. Int J Environ Res Public Health 16(23):4679. https://doi.org/10.3390/ijerph16234679
Singh D, Kumar ARS, Goyal VC, Arora M, Allaka NR (2021) Characteristics of meteorological variables and their implications on evaporation in Roorkee (India). HydroResearch 4:47–60. https://doi.org/10.1016/j.hydres.2021.04.002
Stirnberg R et al (2021) Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning. Atmospheric Chem Phys 21(5):3919–3948. https://doi.org/10.5194/acp-21-3919-2021
Sun Y, Ji M, Jin F, Wang H (2021) Public responses to air pollution in Shandong Province using the online complaint data. ISPRS Int J Geo-Inf 10(3):126. https://doi.org/10.3390/ijgi10030126
Tabatabaie T, Amiri F (2021) Assessment of contribution of SO2, CO, and NO2 in different urban land use in Bushehr region, Iran. Arab J Geosci 14(10):833. https://doi.org/10.1007/s12517-021-07164-6
Tian J, Chen D (2010) A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements. Remote Sens Environ 114(2):221–229. https://doi.org/10.1016/j.rse.2009.09.011
Tvinnereim E, Liu X, Jamelske EM (2017) Public perceptions of air pollution and climate change: different manifestations, similar causes, and concerns. Clim Change 140(3–4):399–412. https://doi.org/10.1007/s10584-016-1871-2
van Klompenburg T, Kassahun A, Catal C (2020) Crop yield prediction using machine learning: a systematic literature review. Comput Electron Agric 177:105709. https://doi.org/10.1016/j.compag.2020.105709
Wang H, Wang J, Wang X (2017) An AQI level forecasting model using chi-square test and BP neural network. In Proceedings of the 2nd International Conference on Intelligent Information Processing - IIP’17. Bangkok, Thailand, pp 1–6. https://doi.org/10.1145/3144789.3144817
Yahya K, Zhang Y, Vukovich JM (2014) Real-time air quality forecasting over the southeastern United States using WRF/Chem-MADRID: multiple-year assessment and sensitivity studies. Atmos Environ 92:318–338. https://doi.org/10.1016/j.atmosenv.2014.04.024
Zheng Q, Yang M, Yang J, Zhang Q, Zhang X (2018) Improvement of generalization ability of deep CNN via implicit regularization in two-stage training process. IEEE Access 6:15844–15869. https://doi.org/10.1109/ACCESS.2018.2810849
Zheng Q, Tian X, Yang M, Su H (2019) The email author identification system based on support vector machine (SVM) and analytic hierarchy process (AHP). p 14
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This article is part of the Topical Collection on IoT for sustainable ground water management.
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Sigamani, S., Venkatesan, R. Air quality index prediction with influence of meteorological parameters using machine learning model for IoT application. Arab J Geosci 15, 340 (2022). https://doi.org/10.1007/s12517-022-09578-2
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DOI: https://doi.org/10.1007/s12517-022-09578-2