Effects of spatial sampling schemes and preferential sampling: a focus on air quality
Air quality monitoring is essential for environmental management and public health. The strategic placement of monitoring stations and accurate assessment on the Air Quality Index remains unsolved due to geographical and socio-economic factors. This project aims to explore the various sampling schem...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175293 |
| _version_ | 1826116799776686080 |
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| author | Kwok, Zong Heng |
| author2 | Michele Nguyen |
| author_facet | Michele Nguyen Kwok, Zong Heng |
| author_sort | Kwok, Zong Heng |
| collection | NTU |
| description | Air quality monitoring is essential for environmental management and public health. The strategic placement of monitoring stations and accurate assessment on the Air Quality Index remains unsolved due to geographical and socio-economic factors. This project aims to explore the various sampling schemes, preferential factors, and predictive methodologies in assessing AQI distribution and optimizing air monitoring networks. As we adopt stratified and random sampling, followed by kriging interpolation, we were able to analyze AQI variability across different counties. While stratified sampling allows us to capture the diverse environmental characteristics of each county, random sampling provided a wider view of AQI distribution. In addition, the use of machine learning approach, Random Forest Regression model was trained to include preferential factors as a covariate to predict AQI values. Stratified Sampling coupled with kriging revealed specific pollution hotspots, indicating potential areas for a station placement. Furthermore, random sampling provided a general AQI spread, identifying a wider region for possible air monitoring placement. On the other hand, Random Forest Regression model offers predictive insights, which is useful to make predictions in unseen locations. This study offers valuable insights in the effects of sampling strategies and offer valuable guidance in the placement of air monitoring stations. |
| first_indexed | 2024-10-01T04:17:21Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/175293 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:17:21Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1752932024-04-26T15:44:25Z Effects of spatial sampling schemes and preferential sampling: a focus on air quality Kwok, Zong Heng Michele Nguyen School of Computer Science and Engineering michele.nguyen@ntu.edu.sg Computer and Information Science Air quality monitoring is essential for environmental management and public health. The strategic placement of monitoring stations and accurate assessment on the Air Quality Index remains unsolved due to geographical and socio-economic factors. This project aims to explore the various sampling schemes, preferential factors, and predictive methodologies in assessing AQI distribution and optimizing air monitoring networks. As we adopt stratified and random sampling, followed by kriging interpolation, we were able to analyze AQI variability across different counties. While stratified sampling allows us to capture the diverse environmental characteristics of each county, random sampling provided a wider view of AQI distribution. In addition, the use of machine learning approach, Random Forest Regression model was trained to include preferential factors as a covariate to predict AQI values. Stratified Sampling coupled with kriging revealed specific pollution hotspots, indicating potential areas for a station placement. Furthermore, random sampling provided a general AQI spread, identifying a wider region for possible air monitoring placement. On the other hand, Random Forest Regression model offers predictive insights, which is useful to make predictions in unseen locations. This study offers valuable insights in the effects of sampling strategies and offer valuable guidance in the placement of air monitoring stations. Bachelor's degree 2024-04-23T11:11:46Z 2024-04-23T11:11:46Z 2024 Final Year Project (FYP) Kwok, Z. H. (2024). Effects of spatial sampling schemes and preferential sampling: a focus on air quality. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175293 https://hdl.handle.net/10356/175293 en SCSE23-0186 application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Kwok, Zong Heng Effects of spatial sampling schemes and preferential sampling: a focus on air quality |
| title | Effects of spatial sampling schemes and preferential sampling: a focus on air quality |
| title_full | Effects of spatial sampling schemes and preferential sampling: a focus on air quality |
| title_fullStr | Effects of spatial sampling schemes and preferential sampling: a focus on air quality |
| title_full_unstemmed | Effects of spatial sampling schemes and preferential sampling: a focus on air quality |
| title_short | Effects of spatial sampling schemes and preferential sampling: a focus on air quality |
| title_sort | effects of spatial sampling schemes and preferential sampling a focus on air quality |
| topic | Computer and Information Science |
| url | https://hdl.handle.net/10356/175293 |
| work_keys_str_mv | AT kwokzongheng effectsofspatialsamplingschemesandpreferentialsamplingafocusonairquality |