Assessment of the relationship between PM10 and visibility in the separation of synoptic code in Yazd

Dehghan, Mahdi; Omidvar, Kamal; Mozafari, Gholamali; Mazidi, Ahmad

doi:10.22111/jneh.2018.20849.1283

Assessment of the relationship between PM10 and visibility in the separation of synoptic code in Yazd

Document Type : Research Article

Authors

¹ PhD Student of Climatology, Faculty of Geographical Sciences, Yazd University, Yazd, Iran

² Professor of Climatology, Faculty of Geographical Sciences, Yazd University, Yazd, Iran

³ Associate Professor of Climatology, Faculty of Geographical Sciences, Yazd University, Yazd, Iran

10.22111/jneh.2018.20849.1283

Abstract

The particulate matter less than 10 µm (PM10) and visibility are known as two important parameters in researches connected to the tropospheric aerosols and dust so that the air pollution is related to those at the specific time. This study analyzes the relationship between PM10 and visibility whit using evolutional Genetic Algorithm. The area’s case study was Yazd city as representative of central of Iran. Visibilities data whit separation of 05, 06, 07 and 09 synoptic conditions, for 5 years (2010-2015) from Yazd Meteorology Organization; and PM10 data from air pollution control stations connected to Yazd Environment Organization have been catches. To reach mentioned mathematic relations, linear regression equation and several kinds of famous functions have been a comparison; which Gaussian function selects as the best fitness function. The results of this research, were the general equation between PM10 and visibility, PM10 and visibility whit 05 code, also PM10 and visibility connected to 09 synoptic code, using Gaussian function in 1 term; and equation between PM10 and visibility when to happen 06 and 07 synoptic conditions, using Gaussian function in 2 term that has been presentation.

Keywords

References

References (in Persian)

Hejazi, A., Mobasheri M. r., Ahmadyan A., (2014), Enhancement of a Semi-empirical Model using Genetic Algorithm for Estimation of Near Surface Particulate Matter (PM10) Concentration in City of Tehran Using Satellite Images and Weather Data. Geography and Environmental Planning, 25, pp.37-50. [In Persian]

Khoshsima, M., AliakbariBidokhti, A. A., & Ahmadi- Givi F., (2013), Evaluation of aerosol optical depth using visibility and remote sensing data in urban and semi urban areas in Iran. Journal of the Earth and Space Physics, 39, pp.163-174. [In Persian]

Khoshsima, M., SabetGhadam, S., &AliakbariBidokhti A. A., (2015), Estimation of atmospheric particulate matter (PM10) concentration based on remote sensing measurements and meteorological parameters: application of artificial neural network. Journal of the Earth and Space Physics. 41, pp.499-510. [In Persian]

Ranjbaran, M., Ajami, A., Bonjakhi, M., Borzouei, H., &Barzin M., (2015), Study of the relationship between the scattering angle and intensity of the atmospheric particles, reducing horizontal visibility. ICOP & ICPET, 21, pp.1397-1400. [In Persian]

Maghrebi M., &Tajreshi, M., (2011), Investigating the Application of Satellite Sensors in Detecting Particulate Particles in Large Cities. Sharif University of Technology, Departmant of Aerospace Engineering, No 1, pp.38-41. [In Persian]

References (in English)

Baddock, M.C., Strong, C.L., Leys, J.F., Heidenreich, S.K., Tews, E.K., McTainsh, G.H., (2014). A visibility and total suspended dust relationship. Atmospheric Environment, 89, 329-336.

Camino, C., Cuevas, E., Basart, S., Alonso, S., Baldasano, J.M., Terradellas, E., Marticorena, B., RodrIguez, S., Berjon, A., (2015). An empirical equation to estimate mineral dust concentrations from visibility observations in Northern Africa. Aeolian Research, 16, 55–68.

Chepil, W.S., Woodruff, N.P., (1957). Sedimentary characteristics of dust storms: visibility and dust concentration. American Journal of science, 255, 104-114.

Davor, A., Viktor, P., Dragan, P., Mirjana, R., Aleksandra, P., (2013). PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Science of the Total Environment, 443, 511–519.

Dayan, U., Ziv, B., Shoob, T., Enzel, Y., (2008). Suspended dust over southeastern Mediterranean and its relation to atmospheric circulations. International Journal of Climatology, 28, 915–924.

Dimitris, V., Kostas, K., Jaakko, K., Teemu, R., Ari, K., Mikko, K., (2011). Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki. Science of the Total Environment, 409, 1266–1276.

Ganjehkaviri, A., Mohd, M.N., Hosseini, S.E., Barzegaravval, H., (2017) Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction. Energy, 119, 167–177.

Grivas, G., Chaloulakou, A., (2006). Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece. Atmospheric Environment, 40, 1216–1229.

Grzegorz, M., Wioletta, R., Piotr, O., Artur, B., Andrzej, B., (2015). The Impact of Selected Parameters on Visibility: First Results from a Long-Term Campaign in Warsaw, Poland. Atmosphere, 6, 1154-1174. Doi: 10.3390/atmos6081154.

Guillaume, A., Almeida, D., (1986). A model for Saharan dust transport. American Meteorological Society, 25, 903-916.

Maulik, U., Bandyopadhyay, S., (2000) Genetic algorithm-based clustering technique. Pattern Recognition, 33, 1455-1465.

Patterson, E.M., Gillette, D.A., (1977). Measurements of visibility vs. mass concentration for airborne soil particles. Atmospheric Environment, 11, 193–196.

Shao, Y., Yang, Y., Wang, J., Song, Z., Leslie, L.M., Dong, C., Zhang, Z., Lin, Z., Kanai, Y., Yabuki, S., Chun, Y., (2003), Northeast Asian dust storms: Real-time numerical prediction and validation, Journal of Geophysical Research, 108, doi:10.1029/2003JD003667.

Vajanapoom, N., Carl, M., Lucas, M., Dana. L., (2001). Estimation of particulate matter from visibility in Bangkok, Thailand. Journal of Exposure Analysis and Environmental Epidemiology, 11, 97-102.

Wang, Z., Chen, L., Tao, J., Zhang, Y., Su. L., (2010). Satellite-based estimation of regional particulate matter (PM) in Beijing using vertical-and-RH correcting method. Remote Sensing of Environment, 114, 50–63.