The Development of Mathematical Model for Prediction of Particulate Matter Pollutants Concentrations in Sarajevo City, Bosnial-Herzegovina

Full Article - PDF

Published: 2022-01-15

Page: 36-43

L. Salami *

Department of Chemical Engineering, Environmental Engineering Research Unit, Lagos State University, Epe, Lagos State, Nigeria.

*Author to whom correspondence should be addressed.


A high concentration of particulate matter (PM) pollutants is a menace to the environment and proactive steps are required to minimize the concentrations of PM in our environment. This work was carried out to develop mathematical model for prediction of PM pollutants concentrations in Sarajevo city, Bosnia – Hergovina. Linear regression model (LRM), nonlinear polynomial regression model (NPRM) and nonlinear exponential regression model (NERM) were developed using the data generated in Sarajevo city with the aid of an in-built solver tool in Minitab Software version 2017. The developed models were subjected to error evaluation functions analysis in order to determine how they deviated from the experimental data. The error evaluation functions used were average relative error (ARE), sum of error square (ERRSQ), Marquardt’s percent standard deviation (MPSD), hybrid fractional error function (HYBRID), and root means square error (RMSE). Others include sum of absolute errors (EABS), standard error of estimate (SEE) and mean relative percentage error (MRPE). The applicability of the developed models was also investigated using statistical tools which include Pearson product moment correlation coefficient (r), coefficient of determination (R2), coefficient of non-determination (K2), student’s t – test(t - test), equality of variance test (F - test) and chi – square test (x2). The results revealed that based on the error evaluation functions analysis, NERM has the least deviation from experimental data when compared with LRM and NPRM. The statistical analysis also showed that NERM represented the experimental data better when compared with LRM and NPRM hence LRM and NPRM were jettisoned and NERM was adopted for the navigation of the experimental data. It was concluded that the NERM should be used to predict the PM pollutants concentrations in Sarajevo city.

Keywords: Concentrations, development, mathematical model, pollutants and prediction

How to Cite

Salami, L. (2022). The Development of Mathematical Model for Prediction of Particulate Matter Pollutants Concentrations in Sarajevo City, Bosnial-Herzegovina. Asian Basic and Applied Research Journal, 4(1), 36–43. Retrieved from


Download data is not yet available.


United State Environmental Protection Agency (USEPA, 2021). Particulate matter basics; 2021.

Accessed date: December 22, 2021.

Ishwar CY, Ningombam LD. Particulate matter. Encyclopedia of Environmental Health, 2nd edition. 2019;1.

Samir A, Maja MD, Nedis D, Jasenka D. The influence of wind speed, humidity, temperature and air pressure on pollutants concentrations of PM10 – Sarajevo case study using wavelet coherence approach. Proceedings of International Symposium on Telecommunication. 2016;1-6.

Garman H, Terri– Ann B, Shannon LW, David P. Temperature and humidity effects on particulate matter concentration in a sub – tropical climate during winter. International Proceedings of Chemical, Biological and Environmental Engineering. 2017;102:41-49.

Jayamurugan R, Kumaravel B, Palanivelraja S, Chockalingam MP. Influence of temperature, relative humidity and seasonal variability on ambient air quality in a coastal urban area. International Journal of Atmospheric Science. 2013;1-7.

Giri D, Krishna MV, Adhikary PR. The influence of meteorological conditions on PM10 concentrations in Kathmandu valley. International Journal of Environmental Research. 2008; 2(1):49 – 60.

Ziyne C, Danlu C, Chuanfeng Z, Mei–po K, Jun C, Yan Z, Bo Z, Yiaoyan W, Bin C, Jing Y, Ruiyuan L, Bin H, Bingbo G, Kaicun W, Bing X. Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism. Environmental International. 2020;139:1 – 20.

Grzegory M, Malgorzata K, Andrzej B. Seasonal variation of particulate matter mass concentration and content of metals. Polish Jiurnal of Environmental Studies. 2011;20(2):417 – 427.

Salami L, Odunlami MO. Air quality assessment of Soluos dumpsite. Nigerian Journal of Industrial System. 2015;12(1):1-11.

Yansui L, Yang, Z, Jiaxin L. Exploring the relationship between air pollurion and meteorological conditions in China under environmental governance. Scientific Reports. 2020;1–7.

Rogers BK, Adewale A, David OE, Precious NE. Particulate matter – based air quality index estimate for Abuja, Nigeria: Implications for health. Journal of Geoscience and Environment Protection. 2020;8:313-321.

Owoade OK, Olis FS, Ogundele LT, Fawole OG, Olaniyi HB. Correlation between particulate matter concentrations and meteorological parameters at a site in Ile – Ife, Nigeria. Ife Journal of Science. 2012;14(1):83-93.

Christopher UO, Tambari GL, Yusuf OL. Influence of meteorological paramters on particle pollution in the tropical climate of Port Harcourt, Nigeria. Archives of Crrent Research International. 2019;19(1):1- 12.

ka IW, Pak KW, Chun SC. Modelling and prediction of particulate matter, NOx and performance of diesel vehicle engine under rare data using relevance vector machine. Journal of Control Science and Engineering. 2012;1-9.

Iryna S. Construction of discrete dynamic model of prediction of particulate matter emission into the air. Computational Problems of Electrical Engineering. 2015;5(1):56 – 59.

Alattar N, Yousif J, Jaffer M, Aljunid SA. Neural and mathematical prediction models for particulate impact on human health in Oman. WSEAS Transaction on Environment and Development. 2021;15:578–585.

Junbeom P, Seongju C. A particulate matter concentrations prediction model based on long short – term memory and an artificial network. International Journal of Environmental Reseaerch and Public Health. 2021;18:1- 15.

Olafadehan OA. Fundamentals of adsorption processes, Lambert Academic Publishing, Mauritus; 2021.

Rebecca B. An introduction to t – tests. Scribbr; 2020.

Access date: December, 2021.

Rahman MM, Pal A, Uddin K, Thu K. Statistical analysis of optimized isotherm model for maxsorb 111 / ethanol and silica gel / water pairs. Journal of Novel Carbon Resources Science and Green Asian Strategy. 2008;5(4):1-12.

Bing L, Yueqiang J, Chaoyang L. Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR – SVR – ARMA combined model. Scientific reports. 2021;11 (348):1- 14.

Licheng Z, Xue T, Yuhan Z, Lulu L. Application of nonlinear land use regression models for ambient air pollutants and air quality index. Atmospheric Pollution Research. 2021; 12(42):101186.

DOI: 10.1016/j.apr.2021.101186.