CORRELATION COEFFICIENT ANALYSIS BETWEEN PM2.5 CONCENTRATIONS AND SOME METEOROLOGICAL PARAMETERS IN IRAQ
DOI:
https://doi.org/10.36103/2twexb46Keywords:
air pollution, Iraq, temperature, wind, pressure, rainfall, Remote sensing, GIS, climate changeAbstract
This study aimed to investigate of the spatial analyses of the correlation coefficient between Particulate matter PM2.5 and meteorological parameters in Iraq Using remote-sensing data and meteorological Parameters datasets for the period 2003–2020. PM2.5 is one of the primary air pollutants across the world. Quantifying interactions between meteorological conditions and PM2.5 concentrations is essential to understanding the variability of PM2.5. The spatial variations of the relationships between the annual average PM2.5, the annual average rainfall, temperature wind speed, and pressure were evaluated using the Pearson correlation coefficient model. The results indicated that there were positive correlations between PM2.5 concentrations and both temperature and wind, while there was a negative correlation with pressure. As for rainfall, there were positive correlations in some areas and negative in others. The results also showed that the most associated factor with PM2.5 is wind. it becomes clear to us that the correlations between fine particles and meteorological factors differ according to the regions, the terrain, the local climate, human and natural differences, and weather changes.
References
Al Ramahi, F. K., O. H. Mutlag, and A. A. Shnain. 2023. The effect of lead isotopes hazards in the soil of Baghdad governorate using remote sensing techniques. AIP Conference Proceedings, 2977, 040060 (2023), 040060-1- 16. https://doi.org/10.1063/5.
Al-Ansari, N. 2021. Topography and climate of Iraq. Journal of Earth Sciences and Geotechnical Engineering, 11(2): 1-13. https://doi.org/10.47260/jesge/1121.
Al-Jbouri, S.Q., and Y.K. Al-Timimi. 2021. Assessment of relationship between land surface temperature and normalized different vegetation index using landsat images in some regions of diyala governorate. Iraqi Journal of Agricultural Sciences, 52(4): 793-801. https://doi.org/10.36103/ijas.v52i4.1388
Al-Khudhairy, A.A., A.H. Shaban, and Y.K. Al-Timimi.2023. Modis Satellite Data Evaluation for Detecting the Dust Storm Using Remote Sensing Techniques over Iraq. IOP Conference Series: Earth and Environmental Science, 1223(1), 012024.https://doi:10.1088/1755-1315/1223/1/012024.
Al-Lami, A. M., Y.K. Al-Timimi, and A.M. Al-Salihi.2024. Innovative trend analysis of annual rainfall in Iraq during 1980-2021.Journal of Agrometeorology, 26(2):196–203 https://doi.org/10.54386/jam.v26i2.2561.
Al-Obaidi, M. A., and Y.K. Al-Timimi.2022. Change detection in Mosul dam lake, north of Iraq using remote sensing and GIS techniques. Iraqi Journal of Agricultural Sciences,53(1):38-47. https://doi.org/10.36103/ijas.v53i1.1506.
Al-Salihi, A. M., A. M. Al-Lami, and A. J Mohammed. 2013. Prediction of monthly rainfall for selected meteorological stations in Iraq using back propagation algorithms. Journal of Environmental Science and Technology, 6(1):16-28. https://doi.org/10.3923/jest.2013.16.28.
Al-Timimi, Y. K., and A.A. Al-Khudhairy. 2018. Spatial and Temporal Temperature trends on Iraq during 1980-2015. Journal of Physics: Conference Series, 1003(1), art.no 012091.
https://doi.org/10.1088/1742-6596/1003/1/012091
Al-Timimi, Y. K. 2021. Monitoring desertification in some regions of Iraq using GIS techniques. Iraqi Journal of Agricultural Sciences,52(3):620-625. https://doi.org/10.36103/ijas.v52i3.1351.
Al-Timimi, Y.K., A.M. Al-Lami, and H.K. Al-Shamarti. 2020. Calculation of the mean annual rainfall in Iraq using several methods in GIS. Plant Archives, 20(2): 1156-1160.
Al-Timimi, Y.K., A.M. AL-Lami, F.S. Basheer, and A. Y. Awad. 2024. Impacts of climate change on thermal bioclimatic indices over Iraq. Iraqi Journal of Agricultural Sciences, 55(2): 744-756. https://doi.org/10.36103/j93nst49
Al-Timimi, Y. K., A.M. Al-Lami, H.K. Al-Shamarti, and S.K. Al-Maamory. 2020. Analysis of some extreme temperature indices over Iraq. Mausam, 71(3): 423-430. https://doi.org/10.54302/mausam.v71i3.40
Al-Timimi, Y. K., and A.A. Al-Khudhairy.2018. Spatial and Temporal Temperature trends on Iraq during 1980-2015.Journal of Physics: Conference Series (JPCS). 1003(1), 012091.
https://doi.org/10.1088/1742-6596/1003/1/012091
Al-Timimi, Y.K., and F. Y. Baktash. 2024. Monitoring the shift of rainfed line of 250 mm over Iraq. Iraqi Journal of Agricultural Sciences, 55(3): 931-940. https://doi.org/10.36103/h10cqh53.
Aalman, A. A., and F. K. Ramahi. 2024. Evaluation and production of predictive maps on the impact of climate factors on the land cover for the Baghdad city for the period (1999-2021). In AIP Conference Proceeding, 2922(1). AIP Publishing. https://doi.org/10.1063/5.0183162.
Azad, A., M. Alam, and M.R. Islam. 2010.Statistical analysis of wind gust at coastal sites of Bangladesh. International Journal of Energy Machinery. 3(1): 9-17.
Banerjee, T., S. B. Singh, and R. K. Srivastava. 2011. Development and performance evaluation of statistical models correlating air pollutants and meteorological variables at Pantnagar, India. Atmospheric Research, 99(3-4), 505-517. https://doi.org/10.1016/j.atmosres.2010.12.003
Evan, A. T., D. J. Vimont, A. K. Heidinger, J. P. Kossin, and R. Bennartz. 2009. The dominant role of aerosols in the evolution of tropical Atlantic Ocean temperature. Science, 324, 778-781.
Garnero, G., and D. Godone.2014. Comparisons between different interpolation techniques. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 139-144. https://doi.org/10.5194/isprsarchives-XL-5-W3-139-2013.
Hassan, S.F. 2018. Modeling Infrastructure Maintenance Contracts in a Geospatial Database. M.Sc. Thesis, Dept. of Software Engineering Coll. Of sci., Tampere University of technology. pp:12.
Hessl, A., J. Miller, J. Kernan, D. Keenum, and D. McKenzie.2007. Mapping paleo-fire boundaries from binary point data: comparing interpolation methods. The Professional Geographer, 59(1):87-104. https://doi.org/10.1111/j.1467-9272.2007.00593.x.
Heumann, C., and M.S. Shalabh. 2016. Introduction to statistics and data analysis. Springer International Publishing Switzerland.
İçağa, Y. and E. Sabah. 2009. Statistical analysis of air pollutants and meteorological parameters in Afyon, Turkey. Environmental modeling and assessment.14(2): 259-266. https://doi.org/10.1007/s10666-008-9139-5.
Jones, P. D., S. C. B. Raper, R. S. Bradley, H. F. Diaz, P. M. Kellyo, and T. M. L. Wigley.1986. Northern Hemisphere surface air temperature variations: 1851–1984. Journal of Applied Meteorology and Climatology, 25(2): 161-179. https://doi.org/10.1175/1520-0450(1986)025<0161:NHSATV>2.0.CO;2.
Kartal, S. and U. Özer. 1998. Determination and parameterization of some air pollutants as a function of meteorological parameters in Kayseri, Turkey. Journal of the Air and Waste Management Association, 48(9): 853-859. https://doi.org/10.1080/10473289.1998.10463738.
Kinney, P.L. 2008.Climate change, air quality, and human health. American journal of preventive medicine.35(5): 459-467. https://doi.org/10.1016/j.amepre.2008.08.025.
Lazaridis, M., and M. Lazaridis. 2011. First principles of meteorology (pp. 67-118). Springer Netherlands.
Li, J., X. Han, M. Jin, X. Zhang, and S. Wang. 2019. Globally analysing spatiotemporal trends of anthropogenic PM2. 5 concentration and population's PM2. 5 exposure from 1998 to 2016. Environment international, 128: 46-62. https://doi.org/10.1016/j.envint.2019.04.026.
Li, L., J. Qian, C.Q. Ou, Y.X. Zhou, C. Guo, and Y. Guo. 2014. Spatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001–2011. Environmental pollution, 190: 75-81. https://doi.org/10.1016/j.envpol.2014.03.020
Pu, W. W., X. J. Zhao, X. L. Zhang, and Z. Q. Ma. 2011. Effect of meteorological factors on PM2. 5 during July to September of Beijing. Procedia Earth and Planetary Science, 2:272-277. https://doi.org/10.1016/j.proeps.2011.09.043
Robinson, T. P., and G. Metternicht.2003. A comparison of inverse distance weighting and ordinary kriging for characterising within-paddock spatial variability of soil properties in Western Australia. Cartography, 32(1):11-24. https://doi.org/10.1080/00690805.2003.9714231.
Salman, A. A., and F. K. Ramahi.2024. Evaluation and production of predictive maps on the impact of climate factors on the the land cover for the Baghdad city for the period (1999-2021). 4th International Conference in Physical Science & Advanced Materials, 2922, 160001-1–160001-12. https://doi.org/10.1063/5.0183162.
Tal-Montaser, Z. N., and A. M. AL-Salihi.2021. Neural Network Based AOD550 and PM10 Estimation Using Limited Meteorological Data in Baghdad City: Case Study. Journal of Green Engineering, 11:1876-1895.
Zhao, Z. D., N. Zhao, and N. Ying. 2021. Association, correlation, and causation among transport variables of PM2. 5. Frontiers in Physics, 9, 684104. https://doi.org/10.3389/fphy.2021.684104.
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