Development and Evaluation of Statistical Models Based on Machine Learning Techniques for Estimating Particulate Matter (PM2.5 and PM10) Concentrations
Abstract
:1. Introduction
- To generate ML-based ANCOVA and random forest regression (RFR) models from Singapore’s air quality and meteorological data for estimating daily PM2.5 and PM10 concentrations in Singapore and to assess the models’ estimation performance;
- To determine the most important explanatory variable that influenced the model outcome;
- To apply and assess the performance of the derived models for estimating daily PM2.5 and PM10 concentrations in Brunei Darussalam.
2. Materials and Methods
2.1. Study Areas
2.2. Data Collection and Preparation
2.3. Machine Learning (ML) Techniques
2.3.1. Analysis of Covariance (ANCOVA) Model
2.3.2. Random Forest Regression (RFR) Model
3. Results and Discussion
3.1. Data Summary
3.2. Estimation Models for PM2.5 Concentration
3.3. Estimation Models for PM10 Concentration
3.4. Application of the Derived Models for Estimating PM2.5 and PM10 Concentrations in Brunei Darussalam
3.4.1. Estimation of PM2.5 Concentration in Brunei Darussalam
3.4.2. Estimation of PM10 Concentration in Brunei Darussalam
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Ghorani-Azam, A.; Riahi-Zanjani, B.; Balali-Mood, M. Effects of air pollution on human health and practical measures for prevention in Iran. J. Res. Med. Sci. 2016, 21, 65. [Google Scholar]
- WHO. Ambient (Outdoor) Air Pollution. 2018. Available online: https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health (accessed on 16 April 2021).
- WHO. Ambient Air Pollution: Pollutants. 2021. Available online: https://www.who.int/airpollution/ambient/pollutants/en/ (accessed on 16 April 2021).
- Patrick, D.L.; Murray, T.P.; Sullivan, R.K., Jr.; Kimmell, K.L. Health & Environmental Effects of Air Pollution. 2016. Available online: https://www.mass.gov/files/documents/2016/08/vl/health-and-env-effects-air-pollutions.pdf (accessed on 16 April 2021).
- WHO. Air Pollution. 2021. Available online: https://www.who.int/southeastasia/health-topics/air-pollution (accessed on 16 April 2021).
- Velasco, E.; Roth, M. Review of Singapore’s air quality and greenhouse gas emissions: Current situation and opportunities. J. Air Waste Manag. Assoc. 2012, 62, 625–641. [Google Scholar] [CrossRef] [Green Version]
- Reddington, C.L.; Yoshioka, M.; Balasubramanian, R.; Ridley, D.; Toh, Y.Y.; Arnold, S.R.; Spracklen, D.V. Contribution of vegetation and peat fires to particulate air pollution in Southeast Asia. Environ. Res. Lett. 2014, 9, 094006. [Google Scholar] [CrossRef] [Green Version]
- Cheong, K.H.; Ngiam, N.J.; Morgan, G.G.; Pek, P.P.; Tan, B.Y.Q.; Lai, J.W.; Koh, J.M.; Ong, M.E.H.; Ho, A.F.W. Acute health impacts of the Southeast Asian transboundary haze problem—A review. Int. J. Environ. Res. Public Health 2019, 16, 3286. [Google Scholar] [CrossRef] [Green Version]
- NEA. Air Quality. 2020. Available online: https://www.nea.gov.sg/our-services/pollution-control/air-pollution/air-quality (accessed on 16 April 2021).
- Emmanuel, S.C. Impact to lung health of haze from forest fires: The Singapore experience. Respirology 2000, 5, 175–182. [Google Scholar] [CrossRef]
- Rybarczyk, Y.; Zalakeviciute, R. Machine learning approaches for outdoor air quality modelling: A systematic review. Appl. Sci. 2018, 8, 2570. [Google Scholar] [CrossRef] [Green Version]
- Riggs, M.R.; Haroldson, K.J.; Hanson, M.A. Analysis of covariance models for data from observational field studies. J. Wildl. Manag. 2008, 72, 34–43. [Google Scholar] [CrossRef]
- Chen, J.-C.; Wang, X.; Serre, M.; Cen, S.; Franklin, M.; Espeland, M. Particulate air pollutants, brain structure, and neurocognitive disorders in older women. Res. Rep. Health Eff. Inst. 2017, 2017, 193. [Google Scholar]
- Xia, Y. Correlation and association analyses in microbiome study integrating multiomics in health and disease. In The Microbiome in Health and Disease; Sun, J., Ed.; Progress in Molecular Biology and Translational Science; Elsevier: Amsterdam, The Netherlands, 2020; Volume 171, pp. 309–491. [Google Scholar]
- Stafoggia, M.; Bellander, T.; Bucci, S.; Davoli, M.; de Hoogh, K.; de’ Donato, F.; Gariazzo, C.; Lyapustin, A.; Michelozzi, P.; Renzi, M.; et al. Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model. Environ. Int. 2019, 124, 170–179. [Google Scholar] [CrossRef]
- Boehmke, B.; Greenwell, B. Random Forests. In Hands-On Machine Learning with R, 1st ed.; Chambers, J.M., Hothorn, T., Lang, D.T., Wickham, H., Eds.; CRC Press: Boca Raton, FL, USA; Taylor & Francis Group: Abingdon-on-Thames, UK, 2020; pp. 203–218. [Google Scholar]
- Doreswamy, H.; Harishkumar, K.S.; Yogesh, K.M.; Ibrahim, G. Forecasting air pollution particulate matter (PM2.5) using machine learning regression models. Procedia Comput. Sci. 2020, 171, 2057–2066. [Google Scholar] [CrossRef]
- Stafoggia, M.; Johansson, C.; Glantz, P.; Renzi, M.; Shtein, A.; de Hoogh, K.; Kloog, I.; Davoli, M.; Michelozzi, P.; Bellander, T. A random forest approach to estimate daily particulate matter, nitrogen dioxide, and ozone at fine spatial resolution in Sweden. Atmosphere 2020, 11, 239. [Google Scholar] [CrossRef] [Green Version]
- Deters, J.K.; Zalakeviciute, R.; Gonzalez, M.; Rybarczyk, Y. Modeling PM2.5 urban pollution using machine learning and selected meteorological parameters. J. Electr. Comput. Eng. 2017, 2017, 5106045. [Google Scholar]
- Dragomir, C.M.; Voiculescu, M.; Constantin, D.-E.; Georgescu, L.P. Prediction of the NO2 concentration data in an urban area using multiple regression and neuronal networks. In AIP Conference Proceedings 1694, Proceedings of the TIM14 Physics Conference—Physics without Frontiers, Timisoara, Romania, 20–22 November 2014; AIP Publishing LLC: Melville, NY, USA, 2015; p. 040003. [Google Scholar]
- DEPS. About Brunei Darussalam. 2020. Available online: http://www.deps.gov.bn/SitePages/Population.aspx (accessed on 16 April 2021).
- Department of Information. About Brunei Darussalam. Available online: http://www.information.gov.bn/SitePages/About%20Brunei%20Darussalam.aspx (accessed on 16 April 2021).
- Singapore Department of Statistics. Population and Population Structure. 2021. Available online: https://www.singstat.gov.sg/find-data/search-by-theme/population/population-and-population-structure/latest-data (accessed on 16 April 2021).
- Singapore Department of Statistics. Environment. 2021. Available online: https://www.singstat.gov.sg/find-data/search-by-theme/society/environment/latest-data (accessed on 16 April 2021).
- David, A.; Hj Sirabaha, H.S.B. Climate of Brunei Darussalam. 2008. Available online: http://bruneiweather.com.bn/dca_dynamic/images/bruneiClimate.pdf (accessed on 16 April 2021).
- MMS. Climate of Singapore. Available online: http://www.weather.gov.sg/climate-climate-of-singapore/ (accessed on 16 April 2021).
- NEA. EPD Report 2018. Available online: https://www.nea.gov.sg/docs/default-source/resource/publications/environmental-protection-division-annual-report/epd-report-2018-v4-(compressed).pdf (accessed on 13 November 2021).
- NUS. Station Information. 2010. Available online: https://inetapps.nus.edu.sg/fas/geog/stationInfo.aspx (accessed on 3 November 2021).
- NEA. FAQs. 2021. Available online: https://www.nea.gov.sg/our-services/pollution-control/air-pollution/faqs#chapterB (accessed on 3 November 2021).
- WHO. WHO Air Quality Guidelines for Particulate Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide: Global Update 2005. 2006. Available online: http://apps.who.int/iris/bitstream/handle/10665/69477/WHO_SDE_PHE_OEH_06.02_eng.pdf;jsessionid=1420FC2A00C78476DA70592F80B32C56?sequence=1 (accessed on 22 August 2021).
- OECD. Exposure to PM2.5 in Countries and Regions. 2021. Available online: https://stats.oecd.org/Index.aspx?DataSetCode=EXP_PM2_5 (accessed on 5 November 2021).
- Addinsoft. XLSTAT Statistical and Data Analysis Solution. 2021. Available online: https://www.xlstat.com/en (accessed on 1 May 2021).
- Seltman, H. Chapter 10: Analysis of covariance. In Experimental Design and Analysis; Carnegie Mellon University: Pittsburgh, PA, USA, 2018; pp. 241–266. Available online: https://www.stat.cmu.edu/~hseltman/309/Book/chapter10.pdf (accessed on 2 June 2021).
- Chew, B.N.; Campbell, J.R.; Salinas, S.V.; Chang, C.W.; Reid, J.S.; Welton, E.J.; Holben, B.N.; Liew, S.C. Aerosol particle vertical distributions and optical properties over Singapore. Atmos. Environ. 2013, 79, 599–613. [Google Scholar] [CrossRef]
- Hansen, A.B.; Witham, C.S.; Chong, W.M.; Kendall, E.; Chew, B.N.; Gan, C.; Hort, M.C.; Lee, S.-Y. Haze in Singapore—Source attribution of biomass burning PM10 from Southeast Asia. Atmos. Chem. Phys. 2019, 19, 5363–5385. [Google Scholar] [CrossRef] [Green Version]
- Yin, S. Biomass burning spatiotemporal variations over South and Southeast Asia. Environ. Int. 2020, 145, 106153. [Google Scholar] [CrossRef]
- Yin, L.; Du, P.; Zhang, M.; Liu, M.; Xu, T.; Song, Y. Estimation of emissions from biomass burning in China (2003–2017) based on MODIS fire radiative energy data. Biogeosciences 2019, 16, 1629–1640. [Google Scholar] [CrossRef] [Green Version]
- Amaral, S.S.; de Carvalho, J.A., Jr.; Costa, M.A.M.; Pinheiro, C. Particulate matter emission factors for biomass combustion. Atmosphere 2016, 7, 141. [Google Scholar] [CrossRef] [Green Version]
- Kiely, L.; Spracklen, D.V.; Wiedinmyer, C.; Conibear, L.; Reddington, C.L.; Archer-Nicholls, S.; Lowe, D.; Arnold, S.R.; Knote, C.; Khan, M.F.; et al. New estimate of particulate emissions from Indonesian peat fires in 2015. Atmos. Chem. Phys. 2019, 19, 11105–11121. [Google Scholar] [CrossRef] [Green Version]
- Borneo Bulletin. Brunei’s Air Quality ‘Moderate’. 2019. Available online: https://borneobulletin.com.bn/bruneis-air-quality-moderate/ (accessed on 20 June 2021).
- Godoy, A.R.L.; da Silva, A.E.A.; Bueno, M.C.; Pozza, S.A.; Coelho, G.P. Application of machine learning algorithms to PM2.5 concentration analysis in the state of São Paulo, Brazil. Braz. J. Environ. Sci. 2021, 56, 152–165. [Google Scholar] [CrossRef]
- De Foy, B.; Schauer, J.J. Changes in speciated PM2.5 concentrations in Fresno, California, due to NOx reductions and variations in diurnal emission profiles by day of week. Elem. Sci. Anth. 2019, 7, 45. [Google Scholar] [CrossRef]
- Guerra, S.A.; Lane, D.D.; Marotz, G.A.; Carter, R.E.; Hohl, C.M.; Baldauf, R.W. Effects of wind direction on coarse and fine particulate matter concentrations in Southeast Kansas. J. Air Waste Manag. Assoc. 2006, 56, 1525–1531. [Google Scholar] [CrossRef] [Green Version]
- Ferenczi, Z.; Imre, K.; Lakatoas, M.; Molnár, Á.; Bozó, L.; Homolya, E.; Gelencsér, A. Long-term characterization of urban PM10 in Hungary. Aerosol Air Qual. Res. 2021, 21, 210048. [Google Scholar] [CrossRef]
- Fortelli, A.; Scafetta, N.; Mazzarella, A. Influence of synoptic and local atmospheric patterns on PM10 air pollution levels: A model application to Naples (Italy). Atmos. Environ. 2016, 143, 218–228. [Google Scholar] [CrossRef] [Green Version]
- Karimian, H.; Li, Q.; Wu, C.; Qi, Y.; Mo, Y.; Chen, G.; Zhang, X.; Sachdeva, S. Evaluation of different machine learning approaches to forecasting PM2.5 mass concentrations. Aerosol Air Qual. Res. 2019, 19, 1400–1410. [Google Scholar] [CrossRef] [Green Version]
- Lee, M.; Lin, L.; Chen, C.-Y.; Tsao, Y.; Yao, T.-H.; Fei, M.-H.; Fang, S.-H. Forecasting air quality in Taiwan by using machine learning. Sci. Rep. 2020, 10, 4153. [Google Scholar] [CrossRef]
- Jacob, D.J.; Winner, D.A. Effect of climate change on air quality. Atmos. Environ. 2009, 43, 51–63. [Google Scholar] [CrossRef] [Green Version]
- Naeher, L.P.; Brauer, M.; Lipsett, M.; Zelikoff, J.T.; Simpson, C.D.; Koenig, J.Q.; Smith, K.R. Woodsmoke health effects: A review. Inhal. Toxicol. 2007, 19, 67–106. [Google Scholar] [CrossRef] [PubMed]
- Hänninen, O.O.; Salonen, R.O.; Koistinen, K.; Lanki, T.; Barregard, L.; Jantunen, M. Population exposure to fine particles and estimated excess mortality in Finland from an East European wildfire episode. J. Expo. Sci. Environ. Epidemiol. 2008, 19, 414–422. [Google Scholar] [CrossRef] [Green Version]
- Abdullah, S.; Mohd Napi, N.N.L.; Ahmed, A.N.; Wan Mansor, W.N.; Abu Mansor, A.; Ismail, M.; Abdullah, A.M.; Ramly, Z.T.A. Development of multiple linear regression for particulate matter (PM10) forecasting during episodic transboundary haze event in Malaysia. Atmosphere 2020, 11, 289. [Google Scholar] [CrossRef] [Green Version]
- Vadrevu, K.; Ohara, T.; Justice, C. Land cover, land use changes and air pollution in Asia: A synthesis. Environ. Res. Lett. 2017, 12, 120201. [Google Scholar] [CrossRef] [Green Version]
- Ministry of the Environment and Water Resources. The Singapore Green Plan 2012; Foo, S.L., Ed.; Ministry of the Environment and Water Resources: Singapore, 2006. Available online: https://www.cbd.int/doc/world/sg/sg-nbsap-v2-en.pdf (accessed on 3 November 2021).
- Nyhan, M.; Sobolevsky, S.; Kang, C.; Robinson, P.; Corti, A.; Szell, M.; Streets, D.; Lu, Z.; Britter, R.; Barrett, S.R.; et al. Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model. Atmos. Environ. 2016, 140, 352–363. [Google Scholar] [CrossRef]
- Mohsin, R.; Majid, Z.A. Comparative study on exhaust emission and engine performance of single cylinder spark-ignited engine operated on gasoline and natural gas. Int. J. Adv. Eng. Appl. 2012, 1, 35–43. [Google Scholar]
- Sentian, J.; Herman, F.; Yih, C.Y.; Wui, J.C.H. Long-term air pollution trend analysis in Malaysia. Int. J. Environ. Impacts 2019, 2, 309–324. [Google Scholar] [CrossRef]
- Ahmad Shuhaili, A.F.; Ihsan, S.I.; Faris, W.F. Air pollution study of vehicles emission in high volume traffic: Selangor, Malaysia as a case study. WSEAS Trans. Syst. 2013, 12, 67–84. [Google Scholar]
Country | Period | Variable | Minimum | Maximum | Mean | Standard Deviation |
---|---|---|---|---|---|---|
Singapore | March 2016 to February 2018 (2 years) | Observed PM2.5 (µg/m3) | 4.38 | 63.84 | 14.46 | 5.39 |
Observed PM10 (µg/m3) | 11.38 | 54.80 | 26.00 | 6.71 | ||
Air temperature, (°C) | 22.51 | 30.27 | 27.53 | 1.16 | ||
Wind speed, (m/s) | 0.50 | 5.98 | 2.21 | 0.85 | ||
Wind direction, (°) | 17.63 | 332.85 | 140.39 | 67.13 | ||
Rainfall, (mm) | 0 | 5.32 | 0.32 | 0.66 | ||
Brunei Darussalam | January 2009 to December 2019 (11 years) | Theoretical PM2.5 (µg/m3) | 2.45 | 42.93 | 7.67 | 3.73 |
Observed PM10 (µg/m3) | 5.75 | 100.90 | 18.02 | 8.76 | ||
Air temperature, (°C) | 23.20 | 31.00 | 27.79 | 1.03 | ||
Wind speed, (m/s) | 1.05 | 7.05 | 2.35 | 0.62 | ||
Wind direction, (°) | 1.96 | 33.13 | 20.43 | 3.59 | ||
Rainfall, (mm) | 0 | 11.46 | 0.37 | 0.79 |
Explanatory Variable | Degree of Freedom (DF) | Sum of Squares of the Errors (SSE) (µg/m3) | Mean Squared Error (MSE) (µg/m3) | Value | Probability > Value |
---|---|---|---|---|---|
Day × Air quality condition | 2 | 52.54 | 26.27 | 3.53 | 0.03 |
Year × Wind speed | 1 | 6.32 | 6.32 | 0.85 | 0.36 |
Year × Air quality condition | 2 | 193.05 | 96.53 | 12.95 | 3.16 × |
Air temperature × Wind speed | 1 | 3.79 | 3.79 | 0.51 | 0.48 |
Air temperature × Wind direction | 1 | 0 | 0 | <0.0001 | 1 |
Air temperature × PM10 | 1 | 16.59 | 16.59 | 2.23 | 0.14 |
Wind direction × PM10 | 1 | 0 | 0 | <0.0001 | 1 |
PM10 × Air quality condition | 1 | 171.98 | 171.98 | 23.08 | 2.00 × |
Air Quality Condition|Monsoon Season | Statistical Indicator | PM2.5 | PM10 | ||
---|---|---|---|---|---|
ANCOVA Model | RFR Model | ANCOVA Model | RFR Model | ||
Overall | 0.75 | 0.89 | 0.81 | 0.93 | |
RMSE (µg/m3) | 0.10 | 0.07 | 0.11 | 0.07 | |
Good|Inter-Monsoon 1 | 0.20 | 0.74 | 0.18 | 0.76 | |
RMSE (µg/m3) | 1.95 | 1.13 | 2.19 | 1.09 | |
Good|Inter-Monsoon 2 | 0.39 | 0.88 | 0.38 | 0.90 | |
RMSE (µg/m3) | 1.95 | 0.96 | 2.20 | 1.00 | |
Good|NE Monsoon | 0.61 | 0.85 | 0.63 | 0.89 | |
RMSE (µg/m3) | 1.58 | 1.02 | 2.65 | 1.45 | |
Good|SW Monsoon | 0.35 | 0.76 | 0.38 | 0.82 | |
RMSE (µg/m3) | 1.65 | 1.01 | 1.93 | 1.06 | |
Moderate|Inter-Monsoon 1 | 0.61 | 0.72 | 0.56 | 0.84 | |
RMSE (µg/m3) | 3.19 | 2.85 | 3.98 | 2.59 | |
Moderate|Inter-Monsoon 2 | 0.78 | 0.85 | 0.76 | 0.87 | |
RMSE (µg/m3) | 2.43 | 2.15 | 2.86 | 2.15 | |
Moderate|NE Monsoon | 0.57 | 0.77 | 0.61 | 0.87 | |
RMSE (µg/m3) | 2.83 | 2.05 | 3.46 | 2.03 | |
Moderate|SW Monsoon | 0.50 | 0.87 | 0.53 | 0.84 | |
RMSE (µg/m3) | 4.08 | 2.09 | 3.38 | 2.05 |
Explanatory Variable | Degree of Freedom (DF) | Sum of Squares of the Errors (SSE) (µg/m3) | Mean Squared Error (MSE) (µg/m3) | Value | Probability > |
---|---|---|---|---|---|
Year × Air temperature | 1 | 0 | 0 | <0.0001 | 1 |
Year × Wind speed | 1 | 82.77 | 82.77 | 10.38 | 1.35 × |
Year × PM2.5 | 1 | 38.96 | 38.96 | 4.89 | 0.03 |
Air temperature × Wind speed | 1 | 2.08 | 2.08 | 0.26 | 0.61 |
Air temperature × PM2.5 | 1 | 145.60 | 145.60 | 18.26 | 2.26 × |
Wind speed × PM2.5 | 1 | 62.66 | 62.66 | 7.86 | 5.23 × |
Wind speed × Air quality condition | 2 | 182.57 | 91.29 | 11.45 | 1.34 × |
Wind direction × PM2.5 | 1 | 156.94 | 156.94 | 19.69 | 1.10 × |
Wind direction × Air quality condition | 2 | 204.49 | 102.25 | 12.83 | 3.58 × |
PM2.5 × Monsoon season | 3 | 97.09 | 32.36 | 4.06 | 7.19 × |
Air Quality Condition|Monsoon Season | Statistical Indicator | PM2.5 | PM10 | ||
---|---|---|---|---|---|
ANCOVA Model | RFR Model | ANCOVA Model | RFR Model | ||
Overall | 0.94 | 0.92 | 0.72 | 0.86 | |
RMSE (µg/m3) | 0.05 | 0.04 | 0.09 | 0.08 | |
Good|Inter-Monsoon 1 | 0.84 | 0.94 | 0.49 | 0.87 | |
RMSE (µg/m3) | 2.56 | 2.11 | 4.54 | 2.25 | |
Good|Inter-Monsoon 2 | 0.85 | 0.90 | 0.49 | 0.80 | |
RMSE (µg/m3) | 2.72 | 2.29 | 4.66 | 2.93 | |
Good|NE Monsoon | 0.78 | 0.93 | 0.57 | 0.85 | |
RMSE (µg/m3) | 2.49 | 1.91 | 3.70 | 2.13 | |
Good|SW Monsoon | 0.94 | 0.91 | 0.68 | 0.81 | |
RMSE (µg/m3) | 3.76 | 3.04 | 6.16 | 5.17 | |
Moderate|SW Monsoon | 0.96 | 0.07 | 0.78 | 0.69 | |
RMSE (µg/m3) | 13.24 | 6.55 | 32.55 | 30.55 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hong, W.Y.; Koh, D.; Yu, L.E. Development and Evaluation of Statistical Models Based on Machine Learning Techniques for Estimating Particulate Matter (PM2.5 and PM10) Concentrations. Int. J. Environ. Res. Public Health 2022, 19, 7728. https://doi.org/10.3390/ijerph19137728
Hong WY, Koh D, Yu LE. Development and Evaluation of Statistical Models Based on Machine Learning Techniques for Estimating Particulate Matter (PM2.5 and PM10) Concentrations. International Journal of Environmental Research and Public Health. 2022; 19(13):7728. https://doi.org/10.3390/ijerph19137728
Chicago/Turabian StyleHong, Wan Yun, David Koh, and Liya E. Yu. 2022. "Development and Evaluation of Statistical Models Based on Machine Learning Techniques for Estimating Particulate Matter (PM2.5 and PM10) Concentrations" International Journal of Environmental Research and Public Health 19, no. 13: 7728. https://doi.org/10.3390/ijerph19137728
APA StyleHong, W. Y., Koh, D., & Yu, L. E. (2022). Development and Evaluation of Statistical Models Based on Machine Learning Techniques for Estimating Particulate Matter (PM2.5 and PM10) Concentrations. International Journal of Environmental Research and Public Health, 19(13), 7728. https://doi.org/10.3390/ijerph19137728