Abstract
Nowadays, increasing particulate matter (PM) remains a challenge for environmental and humanity health and increases death statistics. Case studies and experimental observations demonstrated that vegetation coverage and type of plants affect PM and air quality. Condensation of PM2.5 has an impressive effect on deteriorating air quality. Increasing vegetation coverage has a significant impact on reducing PM2.5. However, the requirement percent vegetation cover (PVC) is likewise a shadow for careful analysis to recommend the requirement percent and area of vegetation for different parts of the metropolitan. In this paper, we propose a four-phase intelligent algorithm for investigating PM2.5 and critical situations to detect unhealthy air quality monitoring stations (AQMSs). Our algorithm makes a decision based on fuzzy and neural network methods and recommends the percent density and area of vegetation. Our analysis of the weather condition is event-driven, considering rainfall as an event to examine the situation of each AQMS before and after rainfall. The experiments demonstrate reducing PM2.5 > 150 to PM2.5 < 50 using recommending PVC of approximately 20–74%. We achieved these results by periodically estimating and evaluating weather conditions in the autumn and winter as two critical seasons of the year.
Similar content being viewed by others
Data availability
References
Bakri MI, Zakaria A, Zakaria SM, Kamarudin LM, Shakaff AY, Saad FS, Ibrahim MF, Razali MH. (2014) Plant bio-absorber for ammonia gas absorption using I2C interface data acquisition system. International Conference on Electronic Design (ICED) Aug 19 (pp. 488-492). https://doi.org/10.1109/ICED.2014.7015856
Ban M, Yu J, Shahidehpour M, Guo D, Yao Y (2018) Considering the differentiating health impacts of fuel emissions in optimal generation scheduling. IEEE Transactions on Sustainable Energy 11(1):15–26. https://doi.org/10.1109/TSTE.2018.2879566
Boppana L, Rani S, Kodali RK. (2019) RFID based vehicle emission monitoring and notification system. IEEE Region 10 Conference (TENCON) Oct 17 (pp. 1264-1269). https://doi.org/10.1109/TENCON.2019.8929488
Chen Y, Jiang C, Wen C, Wu H, Tang Y. (2019) Simulations of traffic control managements on PM2. 5 emissions reduction. International Conference on Transportation Information and Safety (ICTIS) Jul 14 (pp. 475-482). https://doi.org/10.1109/ICTIS.2019.8883725
Dimitriou K, Liakakou E, Lianou M, Psiloglou B, Kassomenos P, Mihalopoulos N, Gerasopoulos E (2020) Implementation of an aggregate index to elucidate the influence of atmospheric synoptic conditions on air quality in Athens, Greece. Air Quality, Atmosphere & Health 1:1–2. https://doi.org/10.1007/s11869-020-00810-0
El-Kader A, Mohamed M, El Hadidi B, Sherif AO. (2008) Seasonal evaluation of temperature inversion over Cairo. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Jul 7 (Vol. 4, pp. IV-601). https://doi.org/10.1109/IGARSS.2008.4779793
Galindo N, Yubero E, Nicolás JF, Varea M, Clemente Á (2018) Day-night variability of PM 10 components at a Mediterranean urban site during winter. Air Quality, Atmosphere & Health (AQAH) 11(10):1251–1258. https://doi.org/10.1007/s11869-018-0627-8
Ha QP, Metia S, Phung MD. (2020) Sensing data fusion for enhanced indoor air quality monitoring. arXiv preprint arXiv:2001.01976. Jan 7. https://arxiv.org/ct?url=https%3A%2F%2Fdx.doi.org%2F10.1109%2FJSEN.2020.2964396&v=c7b1e80d
Huang J, Duan N, Ji P, Ma C, Ding Y, Yu Y, Zhou Q, Sun W (2018) A crowdsource-based sensing system for monitoring fine-grained air quality in urban environments. IEEE Internet of Things Journal (JIOT). Nov 14;6(2):3240-7. https://doi.org/10.1109/JIOT.2018.2881240
Koc CB, Osmond P, Peters A, Irger M (2018) Understanding land surface temperature differences of local climate zones based on airborne remote sensing data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (STARS) 11(8):2724–2730. https://doi.org/10.1109/JSTARS.2018.2815004
Lea PJ, Drake BG, Tobin A, Leitch A. Hall DO, Rao KK. (1999). Photosynthesis. 6th edn. 214 pp. Cambridge: Cambridge University Press.£ 11.95 (softback). Jarvis PG. 1998. European forests and global change: likely impacts of rising CO 2 and temperature. 380 pp. Cambridge: Cambridge University Press.£ 60 (hardback). Möller IM, Gardeström P, Glimelius K, Glaser E, eds. 1998. Plant mitochondria: from gene to function. 603 pp. Leiden, The Netherlands: Backhuys Publishers.£ 95.50 (hardback). Stuessy TF, Ono M. 1998. Evolution and speciation of .... Annals of Botany. 1999 Dec 1;84(6):803–5. https://doi.org/10.1006/anbo.1999.0930
Lee J, Hong Y, Lee Y, Kim HS, Song CH, Kim DY, Jeon M. (2019) Empirical analysis of tree-based models for PM 2.5 concentration prediction. International Conference on Signal Processing and Communication Systems (ICSPCS) Dec 16 (pp. 1-7). https://doi.org/10.1109/ICSPCS47537.2019.9008645
Li J, Chen X, Cui T, Huo H. (2016) Monitoring vegetation green up using satellite and ground data in Inner Mongolia steppe, China. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Jul 10 (pp. 1348–1351). https://doi.org/10.1109/IGARSS.2016.7729343
Liu Z, Wang S (2018) Detecting changes of wheat vegetative growth and their response to climate change over the North China plain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSATRS) 11(12):4630–4636. https://doi.org/10.1109/JSTARS.2018.2870329
Luo J, Barth MJ, Boriboonsomsin K. (2018) Vehicle routing to mitigate human exposure to traffic-related air pollutants. In2018 21st International Conference on Intelligent Transportation Systems (ITSC) Nov 4 (pp. 2765-2770). https://doi.org/10.1109/ITSC.2018.8569501
Mahalingam U, Elangovan K, Dobhal H, Valliappa C, Shrestha S, Kedam G. (2019) A machine learning model for air quality prediction for smart cities. International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Mar 21 (pp. 452-457). https://doi.org/10.1109/WiSPNET45539.2019.9032734
Maheshwari K, Lamba S. (2019) Air quality prediction using supervised regression model. In2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Sep 27 (Vol. 1, pp. 1-7). https://doi.org/10.1109/ICICT46931.2019.8977694
Mayuga GP, Favila C, Oppus C, Macatulad E, Lim LH. (2018) Airborne particulate matter monitoring using uavs for smart cities and urban areas. IEEE Region 10 Conference (TENCON) Oct 28 (pp. 1398-1402). https://doi.org/10.1109/TENCON.2018.8650293
Meng D, Zhicun X, Wu L, Yang Y. (2020) Predict the particulate matter concentrations in 128 cities of China. Air Quality, Atmosphere & Health. Mar 26:1–9. https://doi.org/10.1007/s11869-020-00819-5
Moya TA, van den Dobbelsteen A, Ottele M, Bluyssen PM (2019) A review of green systems within the indoor environment. Indoor and Built Environment (SAGE) 28(3):298–309 10.1177%2F1420326X18783042
Pultarova T (2017) Better hold your breath: London’s killer air. Engineering & Technology 12(4):42–45. https://doi.org/10.1049/et.2017.0403
Qin JL, Yang XH, Luo JT, Fu H, Lei XF, Wei J, Qin YR, Yang ZX (2018) An improved novel nonlinear algorithm of area-wide near-surface air temperature retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11(3):830–844. https://doi.org/10.1109/JSTARS.2018.2793846
Xu Y, Liu H (2020) Spatial ensemble prediction of hourly PM2. 5 concentrations around Beijing railway station in China. Air Quality, Atmosphere & Health 11:1–573. https://doi.org/10.1007/s11869-020-00817-7
Zhang L, Wan X, Sun B. (2019) Tropical natural forest classification using time-series Sentinel-1 and Landsat-8 images in Hainan Island. IEEE International Geoscience and Remote Sensing Symposium (IGRASS) Jul 28 (pp. 6732–6735). https://doi.org/10.1109/IGARSS.2019.8898000
Zheng Z, Chen Y, Wu Z, Qian Q. (2016) Correlation between land surface temperature inversion (based on Landsat-8) and PM 2.5 concentration: taking Guangzhou as an example. International Workshop on Earth Observation and Remote Sensing Applications (EORSA) Jul 4 (pp. 284-289). https://doi.org/10.1109/EORSA.2016.7552814
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they 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.
Rights and permissions
About this article
Cite this article
Rahmani, A.M., Mirmahaleh, S.Y.H. & Hosseinzadeh, M. An intelligent algorithm to recommend percent vegetation cover (ARVC) for PM2.5 reduction. Air Qual Atmos Health 13, 859–870 (2020). https://doi.org/10.1007/s11869-020-00844-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11869-020-00844-4