Downscaling NOx emission into 1 km resolution over a typical mega-city based on POI with machine learning method
Nitrogen oxides (NO _x , NO + NO _2 ) are important air pollutants that significantly impact human health and directly contribute to the formation of ambient ozone and inorganic aerosols. High-resolution NO _x emission inventory is critical for effective pollution management, yet such data often rel...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | Environmental Research Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2515-7620/ade7d8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850117280019513344 |
|---|---|
| author | Zining Yang Hengheng Ge Chun Zhao Xuchao Yang Qiuyan Du Zihan Xia Gudongze Li |
| author_facet | Zining Yang Hengheng Ge Chun Zhao Xuchao Yang Qiuyan Du Zihan Xia Gudongze Li |
| author_sort | Zining Yang |
| collection | DOAJ |
| description | Nitrogen oxides (NO _x , NO + NO _2 ) are important air pollutants that significantly impact human health and directly contribute to the formation of ambient ozone and inorganic aerosols. High-resolution NO _x emission inventory is critical for effective pollution management, yet such data often rely on improper proxies or require extensive preliminary work. Precise disaggregation of emissions based on the latitude-longitude coordinates of emitting facilities is crucial for constructing high-resolution emission inventories. Machine learning methods effectively analyze the association between Point of Interest (POI) and actual emission data, thereby enhancing the accuracy of downscaling process. In this study, we downscaled NO _x emissions from the transport, industry, power plant, and residence sectors in the Multi-resolution Emission Inventory for China (MEIC), originally at 0.25 degree resolution (Low-resolution Inventory, LO), into 1 km resolution (High-resolution Inventory, HI) over Hefei with machine learning that incorporates POI and multi-source remote sensing information. While total emissions in HI and LO are similar, significant spatial variations exist between them. Compared to LO, HI allocates lower emissions to the city center and higher emissions to surrounding areas, thereby providing a more precise representation of emission hotspots. We evaluated both inventories using WRF-Chem and compared the simulated results against ground-based NO _2 observations. The HI-based simulations showed better agreement with observations, with spatial correlation coefficients based on HI and LO were 0.72 and 0.19, respectively. The normalized mean bias (NMB) between simulated and observed NO _2 concentrations was −17.25% for HI and −38.68% for LO, indicating that HI-based simulations substantially reduce underestimation bias. These findings indicate that the downscaled 1 km high-resolution emission inventory provides a more accurate representation of NO _x emission distributions in Hefei. Consequently, simulations based on HI more accurately reproduce NO _2 concentrations at urban scales. |
| format | Article |
| id | doaj-art-c5256163d56d4b2984ca1b23e980d0c2 |
| institution | OA Journals |
| issn | 2515-7620 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Communications |
| spelling | doaj-art-c5256163d56d4b2984ca1b23e980d0c22025-08-20T02:36:07ZengIOP PublishingEnvironmental Research Communications2515-76202025-01-017707501110.1088/2515-7620/ade7d8Downscaling NOx emission into 1 km resolution over a typical mega-city based on POI with machine learning methodZining Yang0https://orcid.org/0009-0009-2812-3874Hengheng Ge1Chun Zhao2https://orcid.org/0000-0003-4693-7213Xuchao Yang3Qiuyan Du4Zihan Xia5Gudongze Li6Deep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China , Hefei, People’s Republic of ChinaSchool of Ocean College, Zhejiang University , Zhoushan, 316021, People’s Republic of ChinaDeep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China , Hefei, People’s Republic of China; Laoshan Laboratory, Qingdao, People’s Republic of China; CAS Center for Excellence in Comparative Planetology, University of Science and Technology of China, Hefei, People’s Republic of ChinaSchool of Ocean College, Zhejiang University , Zhoushan, 316021, People’s Republic of China; Key Laboratory of Citie’s Mitigation and Adaptation to Climate Change in Shanghai, China Meteorological Administration, Shanghai, 200030, People’s Republic of ChinaDeep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China , Hefei, People’s Republic of ChinaDeep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China , Hefei, People’s Republic of ChinaDeep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China , Hefei, People’s Republic of ChinaNitrogen oxides (NO _x , NO + NO _2 ) are important air pollutants that significantly impact human health and directly contribute to the formation of ambient ozone and inorganic aerosols. High-resolution NO _x emission inventory is critical for effective pollution management, yet such data often rely on improper proxies or require extensive preliminary work. Precise disaggregation of emissions based on the latitude-longitude coordinates of emitting facilities is crucial for constructing high-resolution emission inventories. Machine learning methods effectively analyze the association between Point of Interest (POI) and actual emission data, thereby enhancing the accuracy of downscaling process. In this study, we downscaled NO _x emissions from the transport, industry, power plant, and residence sectors in the Multi-resolution Emission Inventory for China (MEIC), originally at 0.25 degree resolution (Low-resolution Inventory, LO), into 1 km resolution (High-resolution Inventory, HI) over Hefei with machine learning that incorporates POI and multi-source remote sensing information. While total emissions in HI and LO are similar, significant spatial variations exist between them. Compared to LO, HI allocates lower emissions to the city center and higher emissions to surrounding areas, thereby providing a more precise representation of emission hotspots. We evaluated both inventories using WRF-Chem and compared the simulated results against ground-based NO _2 observations. The HI-based simulations showed better agreement with observations, with spatial correlation coefficients based on HI and LO were 0.72 and 0.19, respectively. The normalized mean bias (NMB) between simulated and observed NO _2 concentrations was −17.25% for HI and −38.68% for LO, indicating that HI-based simulations substantially reduce underestimation bias. These findings indicate that the downscaled 1 km high-resolution emission inventory provides a more accurate representation of NO _x emission distributions in Hefei. Consequently, simulations based on HI more accurately reproduce NO _2 concentrations at urban scales.https://doi.org/10.1088/2515-7620/ade7d8high-resolution emission inventorymachine learningpoint of interest (POI)NOx emissionsdownscaling |
| spellingShingle | Zining Yang Hengheng Ge Chun Zhao Xuchao Yang Qiuyan Du Zihan Xia Gudongze Li Downscaling NOx emission into 1 km resolution over a typical mega-city based on POI with machine learning method Environmental Research Communications high-resolution emission inventory machine learning point of interest (POI) NOx emissions downscaling |
| title | Downscaling NOx emission into 1 km resolution over a typical mega-city based on POI with machine learning method |
| title_full | Downscaling NOx emission into 1 km resolution over a typical mega-city based on POI with machine learning method |
| title_fullStr | Downscaling NOx emission into 1 km resolution over a typical mega-city based on POI with machine learning method |
| title_full_unstemmed | Downscaling NOx emission into 1 km resolution over a typical mega-city based on POI with machine learning method |
| title_short | Downscaling NOx emission into 1 km resolution over a typical mega-city based on POI with machine learning method |
| title_sort | downscaling nox emission into 1 km resolution over a typical mega city based on poi with machine learning method |
| topic | high-resolution emission inventory machine learning point of interest (POI) NOx emissions downscaling |
| url | https://doi.org/10.1088/2515-7620/ade7d8 |
| work_keys_str_mv | AT ziningyang downscalingnoxemissioninto1kmresolutionoveratypicalmegacitybasedonpoiwithmachinelearningmethod AT henghengge downscalingnoxemissioninto1kmresolutionoveratypicalmegacitybasedonpoiwithmachinelearningmethod AT chunzhao downscalingnoxemissioninto1kmresolutionoveratypicalmegacitybasedonpoiwithmachinelearningmethod AT xuchaoyang downscalingnoxemissioninto1kmresolutionoveratypicalmegacitybasedonpoiwithmachinelearningmethod AT qiuyandu downscalingnoxemissioninto1kmresolutionoveratypicalmegacitybasedonpoiwithmachinelearningmethod AT zihanxia downscalingnoxemissioninto1kmresolutionoveratypicalmegacitybasedonpoiwithmachinelearningmethod AT gudongzeli downscalingnoxemissioninto1kmresolutionoveratypicalmegacitybasedonpoiwithmachinelearningmethod |