Assessment of the monsoonal impact of air pollutants and meteorological factors on physicochemical water quality parameters using remote sensing
With growing urbanization, water contamination has become a problem. The water quality is assessed using physicochemical parameters and requires manual collection. Moreover, physicochemical parameters are insufficient for water quality monitoring as heavy rainfalls and abundance of air pollutants ca...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2023-07-01
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| Series: | Journal of Water and Climate Change |
| Subjects: | |
| Online Access: | http://jwcc.iwaponline.com/content/14/7/2164 |
| _version_ | 1797202714971602944 |
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| author | Mehreen Ahmed Rafia Mumtaz Zahid Anwar Syed Mohammad Hassan Zaidi |
| author_facet | Mehreen Ahmed Rafia Mumtaz Zahid Anwar Syed Mohammad Hassan Zaidi |
| author_sort | Mehreen Ahmed |
| collection | DOAJ |
| description | With growing urbanization, water contamination has become a problem. The water quality is assessed using physicochemical parameters and requires manual collection. Moreover, physicochemical parameters are insufficient for water quality monitoring as heavy rainfalls and abundance of air pollutants cause water pollution. Thus, considering natural factors as influencing parameters and the latest technology for easy and global coverage for sampling, water quality monitoring is modified. This study investigates Rawal watershed with (a) physicochemical, (b) air pollutants like nitrogen dioxide (NO2), and (c) meteorological variables like wind speed for June 2018 to September 2022. Correlation and regression analysis are performed. The results show negative correlations for NO2 with total dissolved solids (TDS) (ranging, 0.51–0.85), turbidity (range, 0.53–0.65), pH (range, 0.5–0.75), and dissolved oxygen (DO) (range, 0.5–0.82), and positive correlation with electric conductivity (EC) (range, 0.54–0.85). The regression analysis with LightGBM, multi-layer perceptron (MLP), and support vector machine (SVM) is applied with air pollutants, and meteorological parameters taken as independent variables giving root-mean-square error (RMSE) (ranging, 0.015–0.18). MLP gave an RMSE of 0.18 and 0.003 for TDS and pH, respectively. SVM performed well for DO, turbidity, and EC with RMSE ranging from 0.015 to 0.027. Moreover, floods on August 2022 are taken as a case study.
HIGHLIGHTS
Impact assessment of air pollutants on physicochemical parameters.;
Meteorological features can have a moderate impact on water quality, i.e., wind speed with chl-α, EC, DO, and TDS, and air temperature with DO and TDS in August and September.;
Machine learning approaches, i.e., LightGBM, MLP, and SVM, are applied for the analysis.;
Floods can have a negative impact on water quality introducing an excess of pollutants and nutrients in water.; |
| first_indexed | 2024-03-12T15:24:21Z |
| format | Article |
| id | doaj.art-0678e580a92a470eac290dcdca5b37ff |
| institution | Directory Open Access Journal |
| issn | 2040-2244 2408-9354 |
| language | English |
| last_indexed | 2024-04-24T08:07:50Z |
| publishDate | 2023-07-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Journal of Water and Climate Change |
| spelling | doaj.art-0678e580a92a470eac290dcdca5b37ff2024-04-17T08:30:18ZengIWA PublishingJournal of Water and Climate Change2040-22442408-93542023-07-011472164219010.2166/wcc.2023.500500Assessment of the monsoonal impact of air pollutants and meteorological factors on physicochemical water quality parameters using remote sensingMehreen Ahmed0Rafia Mumtaz1Zahid Anwar2Syed Mohammad Hassan Zaidi3 School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan Department of Computer Science, North Dakota State University (NDSU), Fargo, ND 58102, USA Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (GIKI), Topi, District Swabi, Khyber Pakhtunkhwa 23460, Pakistan With growing urbanization, water contamination has become a problem. The water quality is assessed using physicochemical parameters and requires manual collection. Moreover, physicochemical parameters are insufficient for water quality monitoring as heavy rainfalls and abundance of air pollutants cause water pollution. Thus, considering natural factors as influencing parameters and the latest technology for easy and global coverage for sampling, water quality monitoring is modified. This study investigates Rawal watershed with (a) physicochemical, (b) air pollutants like nitrogen dioxide (NO2), and (c) meteorological variables like wind speed for June 2018 to September 2022. Correlation and regression analysis are performed. The results show negative correlations for NO2 with total dissolved solids (TDS) (ranging, 0.51–0.85), turbidity (range, 0.53–0.65), pH (range, 0.5–0.75), and dissolved oxygen (DO) (range, 0.5–0.82), and positive correlation with electric conductivity (EC) (range, 0.54–0.85). The regression analysis with LightGBM, multi-layer perceptron (MLP), and support vector machine (SVM) is applied with air pollutants, and meteorological parameters taken as independent variables giving root-mean-square error (RMSE) (ranging, 0.015–0.18). MLP gave an RMSE of 0.18 and 0.003 for TDS and pH, respectively. SVM performed well for DO, turbidity, and EC with RMSE ranging from 0.015 to 0.027. Moreover, floods on August 2022 are taken as a case study. HIGHLIGHTS Impact assessment of air pollutants on physicochemical parameters.; Meteorological features can have a moderate impact on water quality, i.e., wind speed with chl-α, EC, DO, and TDS, and air temperature with DO and TDS in August and September.; Machine learning approaches, i.e., LightGBM, MLP, and SVM, are applied for the analysis.; Floods can have a negative impact on water quality introducing an excess of pollutants and nutrients in water.;http://jwcc.iwaponline.com/content/14/7/2164air pollutantscorrelationmeteorologicalsentinelwater quality |
| spellingShingle | Mehreen Ahmed Rafia Mumtaz Zahid Anwar Syed Mohammad Hassan Zaidi Assessment of the monsoonal impact of air pollutants and meteorological factors on physicochemical water quality parameters using remote sensing Journal of Water and Climate Change air pollutants correlation meteorological sentinel water quality |
| title | Assessment of the monsoonal impact of air pollutants and meteorological factors on physicochemical water quality parameters using remote sensing |
| title_full | Assessment of the monsoonal impact of air pollutants and meteorological factors on physicochemical water quality parameters using remote sensing |
| title_fullStr | Assessment of the monsoonal impact of air pollutants and meteorological factors on physicochemical water quality parameters using remote sensing |
| title_full_unstemmed | Assessment of the monsoonal impact of air pollutants and meteorological factors on physicochemical water quality parameters using remote sensing |
| title_short | Assessment of the monsoonal impact of air pollutants and meteorological factors on physicochemical water quality parameters using remote sensing |
| title_sort | assessment of the monsoonal impact of air pollutants and meteorological factors on physicochemical water quality parameters using remote sensing |
| topic | air pollutants correlation meteorological sentinel water quality |
| url | http://jwcc.iwaponline.com/content/14/7/2164 |
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