DOI : https://doi.org/10.18517/ijaseit.14.4.19273

Central Tendency Data Real-Time Acid Rain Measurement to Evaluate Tool’s Performance Using Statistical Analysis

Ardiansyah Ramadhan (1), Indra Chandra (2), Wiwiek Setyawati (3), Dyah Aries Tanti (4), Asri Indrawati (5), Achmad Faiz Alawi (6), Brety Fetrecia Br Karo (7), Viny Aulia Sabilla (8), Anandha Putri Ayu Prihatini (9)
(1) Electrical Engineering (Graduate Program), School of Electrical Engineering, Telkom University, Bandung, Indonesia
(2) Engineering Physics, School of Electrical Engineering, Telkom University, Bandung, Indonesia
(3) The Research Center for Climate and Atmosphere, National Research and Innovation Agency, Bandung, Indonesia
(4) The Research Center for Climate and Atmosphere, National Research and Innovation Agency, Bandung, Indonesia
(5) The Research Center for Climate and Atmosphere, National Research and Innovation Agency, Bandung, Indonesia
(6) Engineering Physics, School of Electrical Engineering, Telkom University, Bandung, Indonesia
(7) Engineering Physics, School of Electrical Engineering, Telkom University, Bandung, Indonesia
(8) Engineering Physics, School of Electrical Engineering, Telkom University, Bandung, Indonesia
(9) Engineering Physics, School of Electrical Engineering, Telkom University, Bandung, Indonesia
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How to cite (IJASEIT) :
Ramadhan, Ardiansyah, et al. “Central Tendency Data Real-Time Acid Rain Measurement to Evaluate Tool’s Performance Using Statistical Analysis”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 4, Aug. 2024, pp. 1161-9, doi:10.18517/ijaseit.14.4.19273.
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Increased population growth has implications for increased industrial and transportation activities. This activity increased gas emissions in the form of Sulfur Dioxide (SO2) and Nitrogen Dioxide (NO2), resulting in acid rain. The measuring instrument, a rain gauge, is used to measure acid rain in Greater Bandung at Telkom University, Indonesia, but it is currently unable to measure in real-time. Therefore, this study aims to measure acid rain in real-time by measuring parameters such as pH, temperature, conductivity, and precipitation to test the acidity contained in rainwater and evaluate the tool's performance with data for March 2022. The analysis uses various statistical methods: anomaly detection, outlier detection, central tendency, person correlation analysis, Mean Absolute Percentage Error, T-test, and Analysis of Variance (ANOVA). Each parameter can be worked out in real-time based on the central tendency and rain dispersion results. If there is only daily rain, then the correlation for each parameter is the highest, namely pH if there is acid rain. The real-time average pH is 6.45; in the laboratory, it is 6.56, so the MAPE value is 5.37 (good category). Even though this tool can work well, it needs to improve the quality of the temperature control in it. Since temperature significantly affects pH, the results show a negative correlation of -0.80 between pH and temperature. In the ANOVA test, the resulting p-value, when compared with data in the laboratory, is >0.05, meaning that the average daily pH does not have a significant difference from the average laboratory test.

A. Indrawati et al., “Characteristics of Acid Deposition in Urban and Sub-Urban Area,” in Springer Proceedings in Physics, Springer Science and Business Media Deutschland GmbH, 2022, pp. 171–182. doi: 10.1007/978-981-19-0308-3_13.

U.S. EPA, “Acid Rain,” U.S. Environmental Protection Agency. Accessed: Jul. 15, 2023. [Online]. Available: https://www.epa.gov/acidrain/what-acid-rain

R. Puji Lestari et al., “Chemical Composition of Wet Deposition in Jakarta, Serpong, Bandung, Kototabang, and Maros During 2015-2019,” ECOLAB, vol. 15, no. 2, pp. 89–100, 2021, doi:10.20886/jklh.2021.15.2.89-100.

Y. Kim and V. Radoias, “Severe Air Pollution Exposure and Long-Term Health Outcomes,” Int J Environ Res Public Health, vol. 19, no. 21, Nov. 2022, doi: 10.3390/ijerph192114019.

G. Syuhada et al., “Impacts of Air Pollution on Health and Cost of Illness in Jakarta, Indonesia,” Int J Environ Res Public Health, vol. 20, no. 4, Feb. 2023, doi: 10.3390/ijerph20042916.

H. Sani, T. Kubota, J. Sumi, and U. Surahman, “Impacts of Air Pollution and Dampness on Occupant Respiratory Health in Unplanned Houses: A Case Study of Bandung, Indonesia,” Atmosphere (Basel), vol. 13, no. 8, Aug. 2022, doi:10.3390/atmos13081272.

L. K. Widya et al., “Comparison of spatial modelling approaches on PM10 and No2 concentration variations: A case study in Surabaya city, Indonesia,” Int J Environ Res Public Health, vol. 17, no. 23, pp. 1–15, Dec. 2020, doi: 10.3390/ijerph17238883.

I. Chandra, K. Nisa, and E. Rosdiana, “Preliminary study: Health risk analysis of PM2.5 and PM10 mass concentrations in Bandung Metropolitan,” in IOP Conference Series: Earth and Environmental Science, IOP Publishing Ltd, Jul. 2021. doi: 10.1088/1755-1315/824/1/012049.

X. Du, L. Zhang, and H. Ijang, “Variation characteristics and correlation analysis of air quality index and acid rain in Dongying City,” Mar. 2021.

X. Chen et al., “Analysis of the Spatio-temporal Changes in Acid Rain and Their Causes in China (1998-2018),” J Resour Ecol, vol. 12, no. 5, pp. 593–599, Sep. 2021, doi: 10.5814/j.issn.1674-764x.2021.05.002.

D. Rievaldo et al., “Effect of Rainfall on The Chemical Composition of Rainwater in Monitoring Acid Deposition In Greater Bandung,” Indonesian Journal of Urban And Environmental Technology, pp. 49–62, Feb. 2023, doi: 10.25105/urbanenvirotech.v6i1.14051.

N. Y. Hasan, Driejana, A. Sulaeman, and H. D. Ariesyady, “Water quality indices for rainwater quality assessment in Bandung urban region,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, Nov. 2019. doi:10.1088/1757-899X/669/1/012044.

W. Zhang, K. Zhao, B. Wan, Z. Liang, W. Xu, and J. Li, “Chromium Transport and Fate in Vadose Zone: Effects of Simulated Acid Rain and Colloidal Types,” Int J Environ Res Public Health, vol. 19, no. 24, Dec. 2022, doi: 10.3390/ijerph192416414.

Z. Liu et al., “Effect of simulated acid rain on soil CO2, CH4 and N2O emissions and microbial communities in an agricultural soil,” Geoderma, vol. 366, May 2020, doi:10.1016/j.geoderma.2020.114222.

M. Zhou, H. Hu, J. Wang, Z. Zhu, and Y. Feng, “Nitric Acid Rain Increased Bacterial Community Diversity in North Subtropical Forest Soil,” Forests, vol. 13, no. 9, Sep. 2022, doi: 10.3390/f13091349.

Y. Zheng, Y. Wang, Y. Zheng, and Y. Li, “Effects of Simulated Acid Rain on Soil Enzyme Activity and Related Chemical Indexes in Woodlands,” Forests, vol. 13, no. 6, Jun. 2022, doi:10.3390/f13060860.

J. Long et al., “The leaching of antimony and arsenic by simulated acid rain in three soil types from the world’s largest antimony mine area,” Environ Geochem Health, vol. 44, no. 12, pp. 4253–4268, Dec. 2022, doi: 10.1007/s10653-021-01188-3.

Z. Liu et al., “Higher sensitivity of microbial network than community structure under acid rain,” Microorganisms, vol. 9, no. 1, pp. 1–18, Jan. 2021, doi: 10.3390/microorganisms9010118.

I. Rodríguez, A. Ortiz, P. Caldevilla, S. Giganto, G. Búrdalo, and M. Fernández-Raga, “Comparison between the Effects of Normal Rain and Acid Rain on Calcareous Stones under Laboratory Simulation,” Hydrology, vol. 10, no. 4, Apr. 2023, doi:10.3390/hydrology10040079.

X. Zhang, I. Hoff, and R. G. Saba, “Response and deterioration mechanism of bitumen under acid rain erosion,” Materials, vol. 14, no. 17, Sep. 2021, doi: 10.3390/ma14174911.

J. Wei, Q. Chen, J. Du, K. Liu, and K. Jiang, “Study on the Durability of Acid Rain Erosion-Resistant Asphalt Mixtures,” Materials, vol. 15, no. 5, Mar. 2022, doi: 10.3390/ma15051849.

J. Wei, Q. Li, K. Liu, and K. Jiang, “Study on Acid Rain Corrosion Resistance and Action Mechanism of Graphene Oxide Modified Epoxy Asphalt,” International Journal of Pavement Research and Technology, 2023, doi: 10.1007/s42947-023-00292-0.

A. M. Hamed and B. I. Wahab, “Measuring the Acid Rain in Heet City of Iraq,” in IOP Conference Series: Earth and Environmental Science, Institute of Physics, Aug. 2022. doi: 10.1088/1755-1315/1060/1/012022.

V. M. Rodríguez-Sánchez, U. Rosas, G. Calva-Vásquez, and E. Sandoval-Zapotitla, “Does acid rain alter the leaf anatomy and photosynthetic pigments in urban trees?,” Plants, vol. 9, no. 7, pp. 1–16, Jul. 2020, doi: 10.3390/plants9070862.

Z. Shi, J. Zhang, Z. Xiao, T. Lu, X. Ren, and H. Wei, “Effects of acid rain on plant growth: A meta-analysis,” Journal of Environmental Management, vol. 297. Academic Press, Nov. 01, 2021. doi:10.1016/j.jenvman.2021.113213.

H. T. T. Pham, A. T. Nguyen, T. T. H. Nguyen, and L. Hens, “Stakeholder Delphi-perception analysis on impacts and responses of acid rain on agricultural ecosystems in the Vietnamese upland,” Environ Dev Sustain, vol. 22, no. 5, pp. 4467–4493, Jun. 2020, doi:10.1007/s10668-019-00393-6.

M. O. Eyankware and B. E. Ephraim, “A Comprehensive Review of Water Quality Monitoring and Assessment in Delta State, Southern Part of Nigeria,” Journal of Environmental and Earth Sciences, vol. 3, no. 1. Bilingual Publishing Co., pp. 16–28, Apr. 01, 2021. doi:10.30564/jees.v3i1.2900.

B. O. Rosseland, “The legacy from the 50 years of acid rain research, forming present and future research and monitoring of ecosystem impact: This article belongs to Ambio’s 50th Anniversary Collection. Theme: Acidification,” Ambio, vol. 50, no. 2, pp. 273–277, Feb. 2021, doi: 10.1007/s13280-020-01408-7.

H. T. T. Pham, A. T. Nguyen, A. T. N. Do, and L. Hens, “Impacts of simulated acid rain on the growth and the yield of soybean (Glycine max (l.) merr.) in the mountains of northern vietnam,” Sustainability (Switzerland), vol. 13, no. 9, May 2021, doi: 10.3390/su13094980.

T. Navrátil et al., “Mercury cycling during acid rain recovery at the forested Lesní potok catchment, Czech Republic,” Hydrol Process, vol. 35, no. 6, Jun. 2021, doi: 10.1002/hyp.14255.

I. Chandra et al., “Peralatan Pemantauan Air Hujan,” P00202208927, Aug. 21, 2022

A. B. Nassif, M. A. Talib, Q. Nasir, and F. M. Dakalbab, “Machine Learning for Anomaly Detection: A Systematic Review,” IEEE Access, vol. 9. Institute of Electrical and Electronics Engineers Inc., pp. 78658–78700, 2021. doi: 10.1109/access.2021.3083060.

H. Su, Z. Wu, H. Zhang, and Q. Du, “Hyperspectral Anomaly Detection: A survey,” IEEE Geosci Remote Sens Mag, vol. 10, no. 1, pp. 64–90, Mar. 2022, doi: 10.1109/mgrs.2021.3105440.

DFROBOT, “SKU: SEN0161.” Accessed: Aug. 30, 2023. [Online]. Available: https://wiki.dfrobot.com/PH_meter_SKU__SEN0161_

DFROBOT, “SKU: DFR0300.” Accessed: Aug. 30, 2023. [Online]. Available: https://wiki.dfrobot.com/Analog_EC_Meter_SKU_DFR0300

DFROBOT, “SKU: DFR0198.” Accessed: Aug. 30, 2023. [Online]. Available: https://wiki.dfrobot.com/Waterproof_DS18B20_Digital_Temperature_Sensor__SKU_DFR0198_

A. Boukerche, L. Zheng, and O. Alfandi, “Outlier Detection: Methods, Models, and Classification,” ACM Comput Surv, vol. 53, no. 3, Jun. 2020, doi: 10.1145/3381028.

S. Seo, “A Review and Comparison of Methods for Detecting Outliers in Univariate Data Sets,” University of Pittsburgh, 2006.

D. Chakrabarty, “Model Describing Central Tendency of Data,” Int J Adv Res Sci Eng Technol, vol. 8, no. 9, 2021, [Online]. Available: www.ijarset.com

D. Salvatore, D. Reagle, and M.-H. New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto, “Theory and Problems of Statistics and Econometrics Second Edition Schaum’s Outline Series,” 2002, doi:10.1036/0071395687.

T. Budiwati, A. Budiyono, W. Setyawati, and A. Indrawati, “Analisis Korelasi Pearson Untuk Unsur-Unsur Kimia Air Hujan Di Bandung,” Jurnal Sains Dirgantara, vol. 7, 2010.

D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput Sci, vol. 7, pp. 1–24, 2021, doi: 10.7717/PEERJ-CS.623.

Z. Yu, M. Guindani, S. F. Grieco, L. Chen, T. C. Holmes, and X. Xu, “Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research,” Neuron, vol. 110, no. 1. Cell Press, pp. 21–35, Jan. 05, 2022. doi:10.1016/j.neuron.2021.10.030.

Q. Liu and L. Wang, “t-Test and ANOVA for data with ceiling and/or floor effects,” Behav Res Methods, vol. 53, no. 1, pp. 264–277, Feb. 2021, doi: 10.3758/s13428-020-01407-2.

D. van den Bergh, E.-J. Wagenmakers, and F. Aust, “Bayesian Repeated-Measures ANOVA: An Updated Methodology Implemented in JASP,” Jun. 2022, doi: 10.31234/osf.io/fb8zn.

S. E. Maxwell and H. D. Delaney, Designing experiments and analyzing data : a model comparison perspective. Lawrence Erlbaum Associates, 2004.

T.-S. Lim and W.-Y. Loh, “A comparison of tests of equality of variances 1,” 1996.

G. Gümüş and F. Balcı, “Working memory for time intervals: Another manifestation of the central tendency effect,” Psychon Bull Rev, Jun. 2023, doi: 10.3758/s13423-023-02324-z.

T. N. Cason, D. Friedman, and E. Hopkins, “An experimental investigation of price dispersion and cycles,” Journal of Political Economy, vol. 129, no. 3, pp. 789–841, Mar. 2021, doi:10.1086/712445.

A. Qamar, Z. Anwar, H. Ali, S. Imran, R. Shaukat, and M. Mujtaba Abbas, “Experimental investigation of dispersion stability and thermophysical properties of ZnO/DIW nanofluids for heat transfer applications,” Alexandria Engineering Journal, vol. 61, no. 5, pp. 4011–4026, May 2022, doi: 10.1016/j.aej.2021.09.028.

A. M. Kozlowski et al., “Heparin in Acid and Alkaline Environments—A Study of the Correlations between Hydrodynamic Properties and Desulphation,” Polysaccharides, vol. 4, no. 2, pp. 88–98, Mar. 2023, doi: 10.3390/polysaccharides4020007.

C. Qiang and Y. Deng, “A new correlation coefficient of mass function in evidence theory and its application in fault diagnosis,” Applied Intelligence, vol. 52, no. 7, pp. 7832–7842, May 2022, doi:10.1007/s10489-021-02797-2.

S. Li and C. Wei, “Hesitant fuzzy linguistic correlation coefficient and its applications in group decision making,” International Journal of Fuzzy Systems, vol. 22, no. 6, pp. 1748–1759, Sep. 2020, doi:10.1007/s40815-020-00876-z.

X. Zhang, Z. Qu, Z. Tang, and M. Iqbal, “pH-temperature coupled regulation for promoted nanofluidic osmotic energy conversion,” Desalination, vol. 572, Mar. 2024, doi: 10.1016/j.desal.2023.117131.

D. Liu et al., “Self-Assembly Study of Block Copolypeptoids in Response to pH and Temperature Stimulation,” Polymers (Basel), vol. 16, no. 8, Apr. 2024, doi: 10.3390/polym16081082.

A. S. Al-Fahal, A. S. Ahmed, A. K. Mohammed, and W. S. Mohammed-Ali, “Evaluation of Rainwater Harvesting Systems for Drinking Water Quality in Iraq,” International Journal of Design and Nature and Ecodynamics, vol. 19, no. 1, pp. 111–117, Feb. 2024, doi:10.18280/ijdne.190113.

J. Yang, S. Rahardja, and P. Fränti, “Mean-shift outlier detection and filtering,” Pattern Recognit, vol. 115, Jul. 2021, doi:10.1016/j.patcog.2021.107874.

H. Schwarzmeier et al., “Theranostic markers for personalized therapy of spider phobia: Methods of a bicentric external cross-validation machine learning approach,” Int J Methods Psychiatr Res, vol. 29, no. 2, Jun. 2020, doi: 10.1002/mpr.1812.

M. J. Hamayel and A. Y. Owda, “A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms,” AI, vol. 2, no. 4, pp. 477–496, Oct. 2021, doi:10.3390/ai2040030.

A. Ramadhan et al., “Real-Time Measurement Analysis of Rainwater Quality Based on the Internet of Things,” in 2023 29th International Conference on Telecommunications (ICT), 2023, pp. 1–6. doi:10.1109/ICT60153.2023.10374038.

H. Li, Z. Wan, and H. He, “Real-Time Residential Demand Response,” IEEE Trans Smart Grid, vol. 11, no. 5, pp. 4144–4154, 2020, doi: 10.1109/TSG.2020.2978061.

W. D. Xu, M. J. Burns, F. Cherqui, and T. D. Fletcher, “Enhancing stormwater control measures using real-time control technology: a review,” Urban Water J, vol. 18, no. 2, pp. 101–114, Feb. 2021, doi:10.1080/1573062X.2020.1857797.

M. Sarrab, S. Pulparambil, and M. Awadalla, “Development of an IoT based real-time traffic monitoring system for city governance,” Glob Transit, vol. 2, pp. 230–245, Jan. 2020, doi:10.1016/j.glt.2020.09.004.

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