SpringerLink
Log in

Ambient air quality assessment using ensemble techniques

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Air pollution is considered as an important concern all over the world. It disturbs the whole environment and produces more harmful effects to human’s healthy life. Relevant statistical reports from World Health Organization notify that air pollution play a major role in cause of diseases like asthma, lung cancer, stroke, early death and premature birth. Apart from diseases pollution also influence dangerous climate, weather conditions and may cause acid rain, global warming, ozone layer depletion, rainfall declines, etc. Therefore, it is essential to take necessary and preventive measures against air pollution. A comprehensive study is required to assess quality of ambient (outdoor) air, based on the observations of the major pollutants concentration drawn from different monitoring stations. Aiming at this problem, we proposed an ensemble based model to assess the air quality of United States from the period 2000 to 2016. In this article, we resolved the issues related to preprocessing of imbalanced dataset and improved the performance of the entire system through ensemble methods. We compared the recommended model with the existing ones. The experimental results show that the suggested model is superior to other systems and yield high accuracy, low error rate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Alderete TL, Habre R, Toledo-Corral CM et al (2017) Longitudinal associations between ambient air pollution with insulin sensitivity, beta-cell function, and adiposity in Los Angeles Latino children. Diabetes 66(7):1789–1796

    Article  Google Scholar 

  • Ali S, Tirumala SS, Sarrafzadeh A (2014) SVM aggregation modelling for spatiotemporal air pollution analysis. In: 17th IEEE international multi topic conference, vol 2014, pp 249e254. https://doi.org/10.1109/INMIC.2014.7097346

  • Al-Janabi S, Mohammad M, Al-Sultan A (2019) A new method for prediction of air pollution based on intelligent computation. Soft Comput 2020(24):661–680

    Google Scholar 

  • Basavaraju S, Gaj S, Sur A (2019) Object memorability prediction using deep learning: location and size bias. J Vis Commun Image Represent 59:117–127. https://doi.org/10.1016/J.JVCIR.2019.01.008

    Article  Google Scholar 

  • Brook RD, Sun Z, Brook JR et al (2016) Extreme air pollution conditions adversely affect blood pressure and insulin resistance: the air pollution and cardiometabolic disease study. Hypertension 67(1):77–85

    Article  Google Scholar 

  • De Vito et al. (2015) Dynamic multivariate regression for on-field calibration of high speed air quality chemical multi-sensor systems. In: Proceedings of AISEM annual conference, pp 1–3

  • Dong M, Yang D, Kuang Y, He D, Erdal S, Kenski D (2009) PM2:5 concentration prediction using hidden semi-Markov model-based times series data mining. Expert Syst Appl 36(5):9046–9055

    Article  Google Scholar 

  • Furnkranz J, Hullermeier E, Vanderlooy S (2006) Binary decomposition methods for multipartite ranking. In: Buntine WL, Grobelnik M, Mladenic D, Shawe-Taylor J (eds) Machine learning and knowledge discovery in databases, vol 5781(1). Springer, Berlin, pp 359–374

    Google Scholar 

  • Gu K, Qiao J, Lin W (2018) Recurrent air quality predictor based on meteorology- and pollution-related factors. IEEE Trans Ind Inform 14(9):3946–3955

    Article  Google Scholar 

  • Haagenson PL (1967) Meteorological and climatological factors affecting Denver air quality. Atmos Environ 13:79e85. https://doi.org/10.1016/0004-6981(79)90247-61979

    Article  Google Scholar 

  • Honda T, Eliot MN, Eaton CB, Whitsel E, Stewart JD, Mu L, Suh H, Szpiro A, Kaufman JD, Vedal S, Wellenius GA (2017) Long-term exposure to residential ambient fine and coarse particulate matter and incident hypertension in post-menopausal women. Environ Int 105:79–85. https://doi.org/10.1016/j.envint.2017.05.009

    Article  Google Scholar 

  • Kang Z, Qu Z (2017) Application of BP neural network optimized by genetic simulated annealing algorithm to prediction of air quality index in Lanzhou. In: Proceedings of computational intelligence and applications (ICCIA), pp 155–160

  • Karimi B, Samadi S (2019) Mortality and hospitalizations due to cardiovascular and respiratory diseases associated with air pollution in Iran: a systematic review and meta-analysis. Atmos Environ 198:438–447

    Article  Google Scholar 

  • Kim JS, Alderete TL, Chen Z et al (2018) Longitudinal associations of in utero and early life near-roadway air pollution with trajectories of childhood body mass index. Environ Health 17(1):64

    Article  Google Scholar 

  • Leksmono NS, Longhurst JWS, Ling KA, Chatterton TJ, Fisher BEA, Irwin JG (2006) Assessment of the relationship between industrial and traffic sources contributing to air quality objective exceedences: a theoretical modelling exercise. Environ Model Softw 21(4):494–500

    Article  Google Scholar 

  • Li L, Zhang X, Holt J, Tian J, Piltner R (2011) Spatiotemporal interpolation methods for air pollution exposure In: proceedings of symposium abstraction reformulation approximation, pp 75–82

  • Li X, Peng L, Yao X, Cui S, Hu Y, You C, Chi T (2017) Long short-term memory neural network for air pollutant concentration predictions: method development and evaluation. Environ Pollut 231:997–1004. https://doi.org/10.1016/j.envpol.2017.08.114

    Article  Google Scholar 

  • Li J, Zhou C, Xu H et al (2019a) Ambient air pollution is associated with HDL (highdensity lipoprotein) dysfunction in healthy adults. Arterioscler Thromb Vasc Biol 39(3):513–522

    Article  Google Scholar 

  • Li H, Wang J, Li R, Lu H (2019b) Novel analysis forecast system based on multi-objective optimization for air quality index. JClean Prod 208:1365–1383. https://doi.org/10.1016/j.jclepro.2018.10.129

    Article  Google Scholar 

  • Ma J, Ding Y, Gan VJL, Lin C, Wan Z (2019) Spatiotemporal prediction of PM2.5 concentrations at different time granularities using IDW-BLSTM. IEEE Access 7:107897e107907. https://doi.org/10.1109/ACCESS.2019.2932445

    Article  Google Scholar 

  • Mahajan S, Liu HM, Tsai TC, Chen LJ (2018) ‘Improving the accuracy and efficiency of PM2.5 forecast service using cluster-based hybrid neural network model’. IEEE Access 6:19193–19204

    Article  Google Scholar 

  • Monteiro A, Russo M, Gama C, Lopes M, Borrego C (2018) How economic crisis influence air quality over Portugal (Lisbon and Porto)? Atmosp Pollut Res 9:439e445. https://doi.org/10.1016/j.apr.2017.11.009

    Article  Google Scholar 

  • Ngo NS, Zhong N, Bao X (2018) The effects of transboundary air pollution following major events in China on air quality in the US: evidence from Chinese New Year and sandstorms. J Environ Manag 12:169e175. https://doi.org/10.1016/j.jenvman.2018.01.057

    Article  Google Scholar 

  • Nieto PJG, Garcia-Gonzalo E, Lasheras FS, de CosJuez FJ (2015) ‘Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Rel Eng Syst Saf 138:219–231

    Article  Google Scholar 

  • Orun A, Elizondo D, Goodyer E, Paluszczyszyn D (2018) Use of Bayesian inference method to model vehicular air pollution in local urban areas. Transp Res Part Transp Environ 63:236e243. https://doi.org/10.1016/j.trd.2018.05.009

    Article  Google Scholar 

  • Pearce JL, Beringer J, Nicholls N, Hyndman RJ, Tapper NJ (2011) Quantifying the influence of local meteorology on air quality using generalized additive models. Atmos Environ 45(6):1328–1336

    Article  Google Scholar 

  • Pedersen M, Halldorsson TI, Olsen SF, Hjortebjerg D, Ketzel M, Grandstrom C, Raaschou Nielsen OR, Sorensen M (2017) Impact of road traffic pollution on preeclampsia and pregnancy-induced hypertensive disorders. Epidemiology 28(1):99–106

    Article  Google Scholar 

  • Pope CA, Burnett RT, Thun MJ, Calle EE, Krewski D, Ito K, Thurston GD (2002) Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA 287(9):1132–1141

    Article  Google Scholar 

  • Rajput TS, Sharma N (2017) Multivariate regression analysis of air quality index for Hyderabad city: forecasting model with hourly frequency. Int J Appl Res 3(8):443–447

    Google Scholar 

  • Reich SL, Gomez DR, Dawidowski LE (1999) Artificial neural network for the identification of unknown air pollution sources. Atmos Environ 33:3045e3052. https://doi.org/10.1016/S1352-2310(98)00418-X

    Article  Google Scholar 

  • Rifkin R, Klautau A (2004) In defense of one-vs-all classification. J Mach Learn Res 5:101–141

    MathSciNet  MATH  Google Scholar 

  • Sears CG, Braun JM, Ryan PH, Yingying X, Werner EF, Lanphear BP, Wellenius GA (2018) The association of traffic-related air and noise pollution with maternal blood pressure and hypertensive disorders of pregnancy in the HOME study cohort. Environ Int 121(2018):574–581. https://doi.org/10.1016/j.envint.2018.09.049

    Article  Google Scholar 

  • Selden TM, Song D (1994) Environmental quality and development: Is there a kuznets curve for air pollution emissions? J Environ Econ Manag 27:147e162. https://doi.org/10.1006/jeem.1994.1031

    Article  Google Scholar 

  • Shang Z, Deng T, He J, Duan X (2019) A novel model for hourly PM2.5 concentration prediction based on CART and EELM. J Sci Total Environ 651:3043–3052. https://doi.org/10.1016/j.scitotenv.2018.10.193

    Article  Google Scholar 

  • Singh KP, Gupta S, Kumar A, Shukla SP (2012) ‘Linear and nonlinear modeling approaches for urban air quality prediction’. Sci Total Environ 426:244–255

    Article  Google Scholar 

  • Thiering E, Markevych I, Bruske I et al (2016) Associations of residential long-term air pollution exposures and satellite-derived greenness with insulin resistance in German adolescents. Environ Health Perspect 124(8):1291–1298

    Article  Google Scholar 

  • Toledo-Corral CM, Alderete TL, Habre R et al (2018) Effects of air pollution exposure on glucose metabolism in Los Angeles minority children. Pediatr Obes 13(1):54–62

    Article  Google Scholar 

  • Vardoulakis S, Kassomenos P (2008) Sources and factors affecting PM10 levels in two European cities: implications for local air quality management. Atmos Environ 42:3949e3963. https://doi.org/10.1016/j.atmosenv.2006.12.021

    Article  Google Scholar 

  • Wang X, Westerdahl D, Chen LC, Wu Y, Hao J, Pan X, Guo X, Zhang KM (2009) Evaluating the air quality impacts of the 2008 Bei**g Olympic Games: on-road emission factors and black carbon profiles. Atmos Environ 43:4535e4543. https://doi.org/10.1016/j.atmosenv.2009.06.054

    Article  Google Scholar 

  • Warburton DE, Bredin SS, Shellington EM et al (2019) A systematic review of the short-term health effects of air pollution in persons living with coronary heart disease. J Clin Med 8(2):274

    Article  Google Scholar 

  • Yang Z, Tang M (2018) Does the increase of public transit fares deteriorate air quality in Bei**g? Transp Res Part Transp Environ 63:49e57. https://doi.org/10.1016/j.trd.2018.04.020

    Article  Google Scholar 

  • Zheng Y, Yi X, Li M, Li R, Shan Z, Chang E, Li T (2015) Forecasting fine-grained air quality based on big data. In: Proceedings of 21th ACM SIGKDD international conference on knowledge. discovery data mining, pp 2267–2276

  • Zhu Y, Zhang C, Liu D, Ha S, Kim SS, Pollack A, Mendola P (2017) Ambient air pollution and risk of gestational hypertension. Am J Epidemiol 186(3):334–343. https://doi.org/10.1093/aje/kwx097

    Article  Google Scholar 

Download references

Acknowledgements

The study supported by FIST grant received from Department of Science and Technology, Government of India (Reference No.:SR/FST/MSI-107/2015(c)).

Author information

Authors and Affiliations

  1. Department of Mathematics, Srinivasa Ramanujan Centre, SASTRA Deemed to be University, Kumbakonam, Tamil Nadu, 612001, India

    D. Narasimhan

  2. Department of CSE, Srinivasa Ramanujan Centre, SASTRA Deemed to be University, Kumbakonam, Tamil Nadu, 612001, India

    M. Vanitha

Authors
  1. D. Narasimhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Vanitha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Narasimhan.

Ethics declarations

Conflict of interest

Authors have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Item

Description

LSVM

Linear support vector machine

RF

Random forest

NB

Naïve Bayes

LR

Logistic regression

KNN

K nearest neighbor

RBFSVM

Radial basis function support vector machine

ppm

Parts per million

ppb

Parts per billion

AQI

Air quality index

EPA

Environmental pollution agency

oob

Out-of-bag

WHO

World Health Organization

CO

Carbon monoxide

NOx

Nitrogen oxides

O3

Ozone

SOx

Sulfur oxides

RMSE

Root-mean-square error

MAE

Mean absolute error

MCC

Matthews correlation coefficient 

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Narasimhan, D., Vanitha, M. Ambient air quality assessment using ensemble techniques. Soft Comput 25, 9943–9956 (2021). https://doi.org/10.1007/s00500-020-05470-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-05470-x

Keywords

Search

Navigation