Methods for Urban Air Pollution Measurement and Forecasting: Challenges, Opportunities, and Solutions
In today’s urban environments, accurately measuring and forecasting air pollution is crucial for combating the effects of pollution. Machine learning (ML) is now a go-to method for making detailed predictions about air pollution levels in cities. In this study, we dive into how air pollution in urba...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/14/9/1441 |
| _version_ | 1827727197243179008 |
|---|---|
| author | Elena Mitreska Jovanovska Victoria Batz Petre Lameski Eftim Zdravevski Michael A. Herzog Vladimir Trajkovik |
| author_facet | Elena Mitreska Jovanovska Victoria Batz Petre Lameski Eftim Zdravevski Michael A. Herzog Vladimir Trajkovik |
| author_sort | Elena Mitreska Jovanovska |
| collection | DOAJ |
| description | In today’s urban environments, accurately measuring and forecasting air pollution is crucial for combating the effects of pollution. Machine learning (ML) is now a go-to method for making detailed predictions about air pollution levels in cities. In this study, we dive into how air pollution in urban settings is measured and predicted. Using the PRISMA methodology, we chose relevant studies from well-known databases such as PubMed, Springer, IEEE, MDPI, and Elsevier. We then looked closely at these papers to see how they use ML algorithms, models, and statistical approaches to measure and predict common urban air pollutants. After a detailed review, we narrowed our selection to 30 papers that fit our research goals best. We share our findings through a thorough comparison of these papers, shedding light on the most frequently predicted air pollutants, the ML models chosen for these predictions, and which ones work best for determining city air quality. We also take a look at Skopje, North Macedonia’s capital, as an example of a city still working on its air pollution measuring and prediction systems. In conclusion, there are solid methods out there for air pollution measurement and prediction. Technological hurdles are no longer a major obstacle, meaning decision-makers have ready-to-use solutions to help tackle the issue of air pollution. |
| first_indexed | 2024-03-10T23:03:10Z |
| format | Article |
| id | doaj.art-0e259e10e18f4d6e949f17502e787661 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T23:03:10Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-0e259e10e18f4d6e949f17502e7876612023-11-19T09:31:21ZengMDPI AGAtmosphere2073-44332023-09-01149144110.3390/atmos14091441Methods for Urban Air Pollution Measurement and Forecasting: Challenges, Opportunities, and SolutionsElena Mitreska Jovanovska0Victoria Batz1Petre Lameski2Eftim Zdravevski3Michael A. Herzog4Vladimir Trajkovik5Faculty of Computer Science and Engineering, Ss Cyril and Methodius University in Skopje, 1000 Skopje, North MacedoniaMagdeburg Faculty of Computer Science, Magdeburg-Stendal University of Applied Sciences, 39011 Magdeburg, GermanyFaculty of Computer Science and Engineering, Ss Cyril and Methodius University in Skopje, 1000 Skopje, North MacedoniaFaculty of Computer Science and Engineering, Ss Cyril and Methodius University in Skopje, 1000 Skopje, North MacedoniaMagdeburg Faculty of Computer Science, Magdeburg-Stendal University of Applied Sciences, 39011 Magdeburg, GermanyFaculty of Computer Science and Engineering, Ss Cyril and Methodius University in Skopje, 1000 Skopje, North MacedoniaIn today’s urban environments, accurately measuring and forecasting air pollution is crucial for combating the effects of pollution. Machine learning (ML) is now a go-to method for making detailed predictions about air pollution levels in cities. In this study, we dive into how air pollution in urban settings is measured and predicted. Using the PRISMA methodology, we chose relevant studies from well-known databases such as PubMed, Springer, IEEE, MDPI, and Elsevier. We then looked closely at these papers to see how they use ML algorithms, models, and statistical approaches to measure and predict common urban air pollutants. After a detailed review, we narrowed our selection to 30 papers that fit our research goals best. We share our findings through a thorough comparison of these papers, shedding light on the most frequently predicted air pollutants, the ML models chosen for these predictions, and which ones work best for determining city air quality. We also take a look at Skopje, North Macedonia’s capital, as an example of a city still working on its air pollution measuring and prediction systems. In conclusion, there are solid methods out there for air pollution measurement and prediction. Technological hurdles are no longer a major obstacle, meaning decision-makers have ready-to-use solutions to help tackle the issue of air pollution.https://www.mdpi.com/2073-4433/14/9/1441air pollution predictionmachine learningair pollutionreview |
| spellingShingle | Elena Mitreska Jovanovska Victoria Batz Petre Lameski Eftim Zdravevski Michael A. Herzog Vladimir Trajkovik Methods for Urban Air Pollution Measurement and Forecasting: Challenges, Opportunities, and Solutions Atmosphere air pollution prediction machine learning air pollution review |
| title | Methods for Urban Air Pollution Measurement and Forecasting: Challenges, Opportunities, and Solutions |
| title_full | Methods for Urban Air Pollution Measurement and Forecasting: Challenges, Opportunities, and Solutions |
| title_fullStr | Methods for Urban Air Pollution Measurement and Forecasting: Challenges, Opportunities, and Solutions |
| title_full_unstemmed | Methods for Urban Air Pollution Measurement and Forecasting: Challenges, Opportunities, and Solutions |
| title_short | Methods for Urban Air Pollution Measurement and Forecasting: Challenges, Opportunities, and Solutions |
| title_sort | methods for urban air pollution measurement and forecasting challenges opportunities and solutions |
| topic | air pollution prediction machine learning air pollution review |
| url | https://www.mdpi.com/2073-4433/14/9/1441 |
| work_keys_str_mv | AT elenamitreskajovanovska methodsforurbanairpollutionmeasurementandforecastingchallengesopportunitiesandsolutions AT victoriabatz methodsforurbanairpollutionmeasurementandforecastingchallengesopportunitiesandsolutions AT petrelameski methodsforurbanairpollutionmeasurementandforecastingchallengesopportunitiesandsolutions AT eftimzdravevski methodsforurbanairpollutionmeasurementandforecastingchallengesopportunitiesandsolutions AT michaelaherzog methodsforurbanairpollutionmeasurementandforecastingchallengesopportunitiesandsolutions AT vladimirtrajkovik methodsforurbanairpollutionmeasurementandforecastingchallengesopportunitiesandsolutions |