Data analysis of the role of air pollutants (nitrate and nitrite oxide) in temperature changes and precipitation of Tabriz synoptic station using multi-layer perceptron neural network machine learning and logistic regression

Document Type : Research Article

Authors

1 faculty of planning and environment sciencesu

2 uni tabriz

Abstract

The purpose of this research is to analyze the role of air pollutants (nitrate and nitrite oxide) in the changes of 24-hour temperature and precipitation elements in Tabriz synoptic station. The materials and data used in this research are from two different sources. The temperature and precipitation data were obtained from the tabriz synoptic meteorological station hourly for a period of 31 years and the data of Tabriz air pollutants (nitrate and nitrite oxide) were obtained from Tabriz environmental organization. In connection with the air pollutant data, it can be said that these data have been simulated by the multi-layer perceptron neural network machine learning R programming language. In the logistic model, temperature and precipitation were selected as dependent variables and nitrate and nitrite oxide concentrations were selected as independent predictor variables. All data were included in the analysis and the logistic model was significant. The chi-square in nitrate and nitrite oxide was calculated as 348.01, which was significant at the error level of less than 0.05. The aforementioned independent variables have been able to correctly explain between 84 and 60 percent of the changes that led to the increase in temperature and decrease in precipitation. 78.2% of the months that had no changes were correctly classified, and 97.2% of the predictions about temperature and precipitation changes were correct. In total, 90.9% of the predictions have been estimated correctly. . The results showed that pollutants have a significant effect on temperature increase and precipitation decrease in Tabriz synoptic station. The highest and lowest levels of nitrate oxide were observed in September and March, nitrite in September and May, temperature in July and January, and precipitation in April and August, respectively

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[1] Jia Q, Wang YP. Relationships between Leaf Area Index and Evapotranspiration and Crop Coefficient of
Hilly Apple Orchard in the Loess Plateau. Water. 2021; 13: 1957.
[2] Huete A, Didan K, Shimabukuro Y, Ratana P, Saleska S, Hutyra L, Yang W, Nemani R, Myneni R. Amazon
rainforests green-up with sunlight in the dry season. Geophysical Research Letters. 2006; 33 (6): 4.
[3] Wright I, Nobre CA, Tomasella J, Da Rocha HR, Roberts J, Vertamatti E, Culf A, Alvala R., Hodnett M,
Ubarana V. Towards a GCM surface parameterization for Amazonia, In: Gash, J., Nobre, C., Roberts, J.,
Victoria, R. (Eds.), Amazon Deforestation and Climate. J. Wiley & Sons, Chichester, UK. 1996; 473–504.
[4] Costa MH, Foley J. Combined effects of deforestation and doubled atmospheric CO 2 on the climate of
Amazonia. Journal of Climate. 2000; 13: 18–34.
[5] Nobre CA, Silva Dias MAF, Culf A, Polcher J, Gash JH, Marengo J, Avissar R. The Amazonian climate. In:
Kabat, P., et al. (Eds.), Vegetation, Water, Humans and the Climate. Springer Verlag, New York. 2004; 79–
92.
[6] Li Y, Li Z, Wu H, Zhou C, Liu X, Leng P, Yang P, Wu W, Tang R, Shang G. Ma L. Biophysical impacts of
earth greening can substantially mitigate regional land surface temperature warming. Nature Communications.
2023; 14: 121.
[7] Shahmordadi S, Ghafarian Malmiri H. Amini M. Extraction of soil moisture index (TVDI) using a scatter
diagram temperature / vegetation and MODIS images. RS & GIS for Natural Resources. 2021; 12 (1): 38-62.
[In Persian]
[8] Chen B, Wu Z, Wang J, Dong J, Guan L, Chen J, Yang K, Xie G. Spatio- temporal prediction of leaf area
index of rubber plantation using HJ-1A/1B CCD images and recurrent neural network. ISPRS Journal of
Photogrammetry and Remote Sensing. 2015; 102: 148– 160.
[9] Naithani KJ, Baldwin DC, Gaines K P, Lin H, Eissenstat DM. Spatial distribution of tree species governs the
spatio- temporal interaction of leaf area index and soil moisture across a forested landscape. Vegetative
Controls on Hydrology. 2013; 8 (3): 12.
[10] Gigante V, Iacobellis V, Manfreda S, Milella P, Portoghese I. Influences of leaf area index estimations on
water balance modeling in a mediterranean semi-arid basin. Natural Hazards and Earth System Science. 2009;
9 (3): 979-991.
[11] Nearing GS, Crow WT, Thorp KR, Moran MS, Reichle R.H, Gupta HV. Assimilating remote sensing
observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation
Experiment. Water Resources Research. 2012; 48: 13 pp.
[12] Yan H, Wang SQ, Billesbach D, Oechel W, Zhang J H, Meyers T, Martin TA, Matamala R, Baldocchi D,
Bohrer G, Dragoni D, Scott R. Global estimation of evapotranspiration using a leaf area index-based surface
energy and water balance model. Remote Sensing of Environment. 2012; 124: 581–595.
[13] Arx G v, Pannatier E G, Thimonier A, Rebetez M. Microclimate in forests with varying leaf area index and
soil moisture: Potential Implications for Seedling Establishment in a Changing Climate. Journal of Ecology.
2013: 1201–1213.
[14] Chen M, Willgoose G R, Saco P M. Investigating the impact of leaf area index temporal variability on soil
moisture predictions using remote sensing vegetation data. Journal of Hydrology. 2015; 522: 274–284.
[15] Li S, Sawada Y. Soil moisture-vegetation interaction from near-global in-situ soil moisture measurements.
Environmental Research Letters. 2022; 17: 114028
[16] MODIS Web.https://modis.gsfc.nasa.gov/data/dataprod/mod15.php (accessed on 21 November 2017).
[17] Davoodi E, Ghasemieh H, Abdollahi Kh, Batelaan O. Evaluation of temporal-spatial variations of soil
moisture balance by Thorenthwaite Matter method (Case study: Behesht Abad basin). RS & GIS for Natural
Resources. 2018; 9 (1): 74-92. [In Persian]
[18] Liu L, Zhang R, Zuo Z. The Relationship between Soil Moisture and LAI in Different Types of Soil in
Central Eastern China. Journal of Hydrometeorology. 2016; 17 (11): 2733–2742
[19] Wang J, Bao Z, Wang G, Liu C, Xie M, Wang B, Zhang J. The Time Lag Effects and Interaction among
Climate, Soil Moisture, and Vegetation from In Situ Monitoring Measurements across China. Remote Sensing.
2024; 16: 2063.
[20] Li W, Wang Y, Yang J, Deng Y. Time-Lag Effect of Vegetation Response to Volumetric Soil Water
Content: A Case Study of Guangdong Province, Southern China. Remote Sensing. 2022; 14: 1301.
[21] Na L, Na R, Bao Y, Zhang J. Time-Lagged Correlation between Soil Moisture and Intra-Annual Dynamics
of Vegetation on the Mongolian Plateau. Remote Sensing. 2021; 13(8):1527.
[22] Mohammadi Motlagh R. GIS applied training. Barg sabz Publications. Third edition. 2016. 464 pages. [In
Persian]
Volume 11, Issue 2
June 2024
Pages 223-234
  • Receive Date: 08 April 2024
  • Revise Date: 17 May 2024
  • Accept Date: 17 June 2024
  • First Publish Date: 21 June 2024
  • Publish Date: 21 June 2024