Atmospheric Environment最新文献

{"title":"Performance evaluation of different cloud products for estimating surface solar radiation","authors":"Dongyue Liu ,&nbsp;Yunbo Lu ,&nbsp;Lunche Wang ,&nbsp;Ming Zhang ,&nbsp;Wenmin Qin ,&nbsp;Lan Feng ,&nbsp;Zhitong Wang","doi":"10.1016/j.atmosenv.2024.121023","DOIUrl":"10.1016/j.atmosenv.2024.121023","url":null,"abstract":"<div><div>The presence and variability of clouds have a significant effect on surface solar radiation (SSR). The range of cloud products currently available for SSR estimation vary in spatial and temporal resolution and accuracy. Since the effect of different cloud products on the accuracy of SSR estimation has not been adequately quantified in existing studies, this study evaluates the performance of four cloud products (Himawari-8, ISCCP, CERES, and MERRA-2) in estimating SSR and analyzes them in comparison with the MODIS cloud product. The accuracy of SSR estimation of the four cloud products is verified using measured data from BSRN and CERN ground-based observatories. The results show that Himawari-8 has the best performance with R-squared (R²) values of 0.94 and 0.74 and root mean square errors (RMSE) of 71.03 W/m² and 141.36 W/m² on the sub-daily scales at the BSRN and CERN sites, respectively. CERES and ISCCP have similar performances, but they vary by site and month. While MERRA-2 grossly underestimates SSR, probably related to misclassification of clear skies as cloudy and overestimation of cloud optical thickness under cloudy conditions. Compared to MODIS, Himawari-8 provides better agreement with MODIS results in cloud classification and cloud phase identification, while CERES provides better agreement with MODIS results in cloud optical thickness. Overall, Himawari-8 performs best in SSR estimation. This comprehensive assessment not only highlights the crucial role of cloud observations on SSR estimations but also details the strengths and weaknesses of each cloud product in enhancing the understanding of solar radiation dynamics.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"344 ","pages":"Article 121023"},"PeriodicalIF":4.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143308501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the ecological risk of surface ozone and its impact on crop yields in China throughout the entire year of the COVID-19 pandemic in 2020","authors":"Hui Zhao ,&nbsp;Jinghan Wang ,&nbsp;Yu Pan ,&nbsp;Qi Guan ,&nbsp;Mingjie kang ,&nbsp;Ting Li","doi":"10.1016/j.atmosenv.2025.121030","DOIUrl":"10.1016/j.atmosenv.2025.121030","url":null,"abstract":"<div><div>In 2020, the outbreak of the novel coronavirus (COVID-19) spread across China and the globe. In response to this severe challenge, China swiftly enforced a series of rigorous lockdown measures, significantly improving air quality. However, O₃ levels increased, and their potential impact on ecosystems remains unclear. Therefore, this research systematically assessed the ecological risks from O₃ during the warm season of 2020 across China and further quantified its effect on the yields of major crops. The findings revealed that during the warm season of 2020, the values of the five ecological risk indicators across China were 42.1 ± 0.5 ppb for M12, 43.0 ± 0.5 ppb for M7, 32.5 ± 1.3 ppm h for SUM06, 22.1 ± 0.7 ppm h for AOT40, and 27.2 ± 1.0 ppm h for W126. The highest risks were observed in the Beijing-Tianjin-Hebei, followed by the Yangtze River Delta and Central China. During the main crop growing seasons, the national average AOT40 values were 9.3 ± 0.3 ppm h for winter wheat, 11.6 ± 0.6 ppm h for spring wheat, 10.2 ± 0.4 ppm h for single rice, 5.8 ± 0.4 ppm h for double-early rice, and 7.9 ± 0.4 ppm h for double-late rice. The projected ranges of O₃-induced national relative yield losses for wheat and rice were 20.4–32.9% and 3.1–9.7%, respectively. Correspondingly, the total national yield losses were 6.61 × 10⁷ metric tons and 1.37 × 10⁷ metric tons, respectively. Our findings reveal that O₃ posed significant harmful risks to ecosystems during the COVID-19 pandemic. These results not only highlight the threat of O₃ to agricultural production but also offer a scientific foundation to develop enhanced policies for controlling air pollution effectively.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"344 ","pages":"Article 121030"},"PeriodicalIF":4.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143308664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of regional PM2.5 concentration in China based on fine-mode aerosol optical thickness (AODf) and study of influencing factors","authors":"Cheng Wan ,&nbsp;Haifeng Xu ,&nbsp;Wenhui Luo ,&nbsp;Jinji Ma ,&nbsp;Zhengqiang Li","doi":"10.1016/j.atmosenv.2025.121026","DOIUrl":"10.1016/j.atmosenv.2025.121026","url":null,"abstract":"<div><div>In recent years, rapid industrialization and urbanization in China have resulted in severe air pollution, with fine particulate matter (PM_2.5) being a major issue. PM_2.5 estimation typically relies on aerosol optical depth (AOD) data, while PM_2.5 is primarily composed of fine-mode aerosols, better represented by fine-mode aerosol optical depth (AODf). This study constructed PM_2.5 estimation models using both AODf and AOD data to obtain long-term PM_2.5 concentration datasets for China. SHAP and biased dependence algorithms were applied to analyze influencing factors and interactions, along with regional differences in PM_2.5 estimation based on multimodal AOD. The results indicate that AODf-based PM_2.5 estimation slightly improves accuracy compared to AOD. PM_2.5 concentrations showed an increasing trend from 2001 to 2013, peaking during this period, followed by a decline after 2013. Seasonally, the highest concentration was observed in winter (64.49 ± 19.8 μg/m³), followed by spring and autumn, with the lowest in summer (33.07 ± 8.8 μg/m³). The main influencing factors include AODf (26.97%), relative humidity (14.33%), 2m temperature (10.75%), and total evaporation (9.93%). Regional differences are evident: in the west, coarse-mode aerosols dominate, limiting the accuracy of AODf-based estimation, while in the east, fine-mode aerosols play a larger role. Furthermore, the continued decline in PM_2.5 is attributed to the decreasing proportion of fine-mode aerosols. This study is of great significance for a comprehensive understanding of the changing pattern of PM_2.5 and the formulation of air pollution control policies according to local conditions.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"344 ","pages":"Article 121026"},"PeriodicalIF":4.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143307701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensitivity analysis of aerosol optical and radiative properties over the climate sensitive Hindu Kush Himalayan region using sky radiometer observation","authors":"Shantikumar S. Ningombam ,&nbsp;Swagata Mukhopadhyay ,&nbsp;B.L. Madhavan ,&nbsp;A.K. Srivastava","doi":"10.1016/j.atmosenv.2024.121008","DOIUrl":"10.1016/j.atmosenv.2024.121008","url":null,"abstract":"<div><div>The study examined sensitivity analysis of aerosol optical and radiative properties due to different versions of SKYRAD.pack module (i.e. versions 4.2 and 5.0) along with stability and performance of sky radiometer instruments (POM-01), operating at Hanle, Leh and Merak, located at high-altitude background sites in the most climate sensitive Hindu Kush Himalayan region. The study utilized long-term aerosol measurements during 2008–2024 for examining the stability and performance of the instruments. As a part of sensitivity analysis, coarse-mode aerosol optical depth (AOD) was found to be higher at version 4.2, while fine-mode AOD showed higher at version 5.0, but interestingly the variation of total AOD was found to be insignificant. Further, single scattering albedo (SSA) at version 5.0 was overestimated from 4.2 version. Among the parameters, aerosol asymmetry parameter (AS) showed significantly larger difference between the two versions with overestimation at 4.2 version. Such large differences of AS may be attributed to variations in aerosol radiative forcing parameters. Further, variation of <span><math><mo>±</mo></math></span>2% calibration constants (F0I) in the sensitivity analysis showed significant variation in the retrieval parameters. Aerosol volume size distribution at three sites showed dominantly tri-modal pattern at version 4.2, while version 5.0 showed dominance of bi-modal distribution, which may be attributed from significant variation of AS between the two versions. These findings highlighted the importance of performing calibration procedures frequently to ensure the quality controlled data at background sites in particular, and sensitivity analysis for aerosol retrieval parameters in different versions of the SKYRAD.pack software tool.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"344 ","pages":"Article 121008"},"PeriodicalIF":4.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143307761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of meteorological conditions and seasonality on PM1 and organic aerosol sources at a rural background site","authors":"Radek Lhotka ,&nbsp;Petra Pokorná ,&nbsp;Petr Vodička ,&nbsp;Naděžda Zíková ,&nbsp;Gang I. Chen ,&nbsp;André S.H. Prévôt ,&nbsp;Saliou Mbengue ,&nbsp;Jaroslav Schwarz ,&nbsp;Vladimír Ždímal","doi":"10.1016/j.atmosenv.2025.121028","DOIUrl":"10.1016/j.atmosenv.2025.121028","url":null,"abstract":"<div><div>Organic aerosols (OA) are a main component of PM_2.5 (20–90%), which contains thousands of compounds. Thus, the formation processes and sources of OA remain poorly understood. In this study, the seasonal and diurnal variabilities of submicron PM chemical composition (both inorganic and organic aerosols) and OA sources were characterized by aerosol mass spectrometry (AMS) and an aethalometer at a rural background site in the Czech Republic (National Atmospheric Observatory Košetice – NAOK) from January to October 2019. The effects of meteorological conditions and local, regional, and long-range atmospheric transport influences in Central Europe were also studied. The overall average submicron PM concentration was 9.26 ± 5.88 μg m⁻³. Using positive matrix factorization (PMF) analysis, we identified four OA factors for summer and five for all other seasons. Three factors were associated with primary sources of OA (POA): hydrocarbon-like OA (HOA), biomass-burning OA (BBOA), and OA from coal burning (CCOA, absent in summer), the CCOA factor enabling a better description of the residential heating effect on the background station in Central Europe. Two remaining factors represented oxygenated OA (OOA) sources: less oxidized OOA (LO-OOA) and more oxidized OOA (MO-OOA).Higher pollution episodes of submicron PM and all OA sources were predominantly associated with continental air masses. The effect of dispersion conditions, as assessed by the ventilation index (VI) and not yet been studied at a rural background site, proved to be a critical factor. The extension of the number of primary factors to include CCOA in PMF analysis, together with the reflection of the influence of seasonality, air mass origin and changes in meteorology, especially dispersion conditions, has elucidated the origin and fate of OA in the atmosphere at this type of European background stations.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"344 ","pages":"Article 121028"},"PeriodicalIF":4.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143308526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of meteorological uncertainties on PM2.5 forecast: An ensemble air quality forecast study during 2022 Beijing Winter Olympics","authors":"Wei Wen ,&nbsp;Liyao Shen ,&nbsp;Li Sheng ,&nbsp;Xin Ma ,&nbsp;Jikang Wang ,&nbsp;Chenggong Guan ,&nbsp;Guo Deng ,&nbsp;Hongqi Li ,&nbsp;Bin Zhou","doi":"10.1016/j.atmosenv.2025.121027","DOIUrl":"10.1016/j.atmosenv.2025.121027","url":null,"abstract":"<div><div>This research constructed an air quality ensemble forecasting model consisting of fifteen members using the China Meteorological Administration regional ensemble forecasting system (CMA_REPS) and Comprehensive Air Quality Model Extensions (CAMx) models to investigate the influence of atmospheric field uncertainty on air quality simulations. Focusing on the Beijing Winter Olympics in February 2022, this study examines the effects of both ground-level and vertical meteorological conditions on PM_2.5 concentration distributions. The simulation accuracy of the model was validated, and its performance was analyzed. Results revealed that the ensemble mean simulations exhibit high correlation coefficients with observations for temperature (0.95), wind speed (0.80), relative humidity (0.83), and pressure (0.99). Both the control forecast and the ensemble mean for PM_2.5 concentration aligned well with observations, with the ensemble mean demonstrating a strong correlation between the root mean square error and ensemble spread. In terms of reducing the false alarm rate (FAR) and improving the Bias Score (BS), the ensemble mean outperformed the control forecast. The control forecast for PM_2.5 concentration was found to be more accurate at and around pollutant concentration inflection points, which may be attributed to simulation deviations in temperature and pressure that introduce uncertainty in atmospheric stability simulations. The correlation between PM_2.5 and various meteorological elements varied during different periods. The vertical distribution of meteorological factors also significantly affected simulation outcomes, particularly uncertainties in simulating wind speed and inversion temperature processes, which further contributed to the uncertainty in pollutant simulations.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"344 ","pages":"Article 121027"},"PeriodicalIF":4.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143308483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Particulate matter and airborne microorganisms in a construction site in Graz, Austria","authors":"Doris Haas ,&nbsp;Sarah R. Pikal ,&nbsp;Herbert Galler ,&nbsp;Juliana Habib ,&nbsp;Tina Moser ,&nbsp;Petra Ofner-Kopeinig ,&nbsp;Michael Schalli","doi":"10.1016/j.atmosenv.2024.121025","DOIUrl":"10.1016/j.atmosenv.2024.121025","url":null,"abstract":"<div><div>During construction work, the number of dust particles in the air can increase, which is also leading to a higher load of bioaerosols being transported. This study measured the concentrations of particulate matter, bacteria and fungi in the area of a large construction site at the Medical University of Graz. The measurements were carried out in the period of 1 year at three measuring sites outdoors and two indoors in a neighboring building. <em>Aspergillus</em> spp., <em>Aspergillus fumigatus (A. fumigatus)</em>, <em>Penicillium</em> spp. and <em>Cladosporium</em> spp., ubiquitous in the air, were considered as indicators for air pollution. The particle concentration was determined by using the APC M3 Airborne Particle Counter. The concentration of microorganisms was measured by MAS-100 NT®. The results showed that on the construction site, the median concentrations of particulate matter (3.6 x 10⁷ m⁻³) were positively correlated with the outdoor measuring sites and the indoor air (2.0 x 10⁷ m⁻³). The fine particles increased at low temperatures, especially in winter and the coarse particles increased in summer. At the construction site, the bacterial load was 1.5 times lower than those in the indoor air in contrast, the fungal spores were significantly higher. It was found that the coarse particles correlated positively with the bacteria and the genus <em>Cladosporium</em> and the fine particles with the genera <em>Penicillium</em> and <em>Aspergillus</em>. At low air temperature the fine particles, the genera <em>Aspergillus</em> and <em>Penicillium</em> increased with altitude. Bacteria and <em>Aspergillus</em> spp. were wind speed dependent. Future studies are needed to investigate dust particle and bioaerosol concentrations during different construction stages.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"344 ","pages":"Article 121025"},"PeriodicalIF":4.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143307760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measurements and model results of a two-year dataset of ammonia exchange over a coniferous forest in the Netherlands","authors":"E.A. Melman ,&nbsp;S. Rutledge-Jonker ,&nbsp;K.F.A. Frumau ,&nbsp;A. Hensen ,&nbsp;W.A.J. van Pul ,&nbsp;A.P. Stolk ,&nbsp;R.J. Wichink Kruit ,&nbsp;M.C. van Zanten","doi":"10.1016/j.atmosenv.2024.120976","DOIUrl":"10.1016/j.atmosenv.2024.120976","url":null,"abstract":"<div><div>In this study we present and analyse a two-year dataset of NH<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span> exchange over a temperate Douglas fir forest in the Netherlands. The atmospheric NH<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span> concentration ([NH<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>]) was measured at multiple heights above the canopy in 2009 and 2010. We applied the aerodynamic gradient method combined with four different methods for roughness sublayer correction to calculate fluxes. The results with and without this correction were on average similar, but instantaneous differences can be up to 30%. We evaluated a 1-D inferential model (DEPAC). The reference run tended to overestimate deposition and did not predict emission. The observed stomatal emission potential (<span><math><msub><mrow><mi>Γ</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span>) agrees well with values from literature and the modelled relation in DEPAC. The model performance strongly improved after implementation of a temperature dependent scaling factor in the external leaf pathway. We estimated the annual deposition load by combining observed and modelled fluxes and subsequent extrapolation of the mean (median) flux to be <span><math><mrow><mn>11</mn><mo>.</mo><mn>8</mn><mo>±</mo><mn>3</mn><mo>.</mo><mn>5</mn></mrow></math></span> (<span><math><mrow><mn>8</mn><mo>.</mo><mn>5</mn><mo>±</mo><mn>2</mn><mo>.</mo><mn>6</mn></mrow></math></span>) kg N ha⁻¹ in 2009 and <span><math><mrow><mn>11</mn><mo>.</mo><mn>4</mn><mo>±</mo><mn>3</mn><mo>.</mo><mn>4</mn></mrow></math></span> (<span><math><mrow><mn>8</mn><mo>.</mo><mn>7</mn><mo>±</mo><mn>2</mn><mo>.</mo><mn>6</mn></mrow></math></span>) kg N ha⁻¹ in 2010. Compared to historical measurements in the nineties at the same site, the [NH<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>] has stayed approximately constant and the deposition has decreased. Further research has to be done to better quantify these trends and to assess how the newly proposed external leaf pathway in DEPAC behaves in large scale transport models.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"344 ","pages":"Article 120976"},"PeriodicalIF":4.2,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143308488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel method for detecting natural dust source regions using satellite and ground-based measurements","authors":"Jae-Hyeong Lee ,&nbsp;Sang-Hyun Lee ,&nbsp;Jeong Hoon Cho","doi":"10.1016/j.atmosenv.2024.121024","DOIUrl":"10.1016/j.atmosenv.2024.121024","url":null,"abstract":"<div><div>This study presents a novel approach for identifying natural dust source regions, utilizing a combination of satellite and ground-based measurements. Unlike previous methods that relied solely on either satellite-derived land-cover characteristics or surface dust observations, this new method harmoniously integrates both. Its strength lies in accurately identifying natural dust source regions and their spatio-temporal variations by reflecting the satellite-based land-cover characteristics, ground vegetation, and snow-cover conditions obtained from natural dust source regions. Vegetation bareness index (B) and snow coverage index (S) were defined to represent the ground conditions, and their threshold values were determined by statistically combining satellite-derived data and ground dust detection records. A comparison of the new method against previous methods in identifying natural dust source regions in East Asia showed that the new method could accurately identify major dust source regions spanning the Tibetan Plateau, inner Mongolia, and the Horqin Desert in northeastern China, along with parts of Russia and Kazakhstan. In contrast, previous satellite-based methods either significantly underestimated the dust source regions, including only the Gobi and Taklimakan deserts, or overestimated by broadly covering the East Asian regions. The new method also proved superior in detecting monthly variation of the East Asian dust source regions due to short vegetation. The findings indicate that this new method effectively overcomes the limitations of previous methods, suggesting being beneficial in natural dust modeling through an accurate representation of natural dust source regions.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"344 ","pages":"Article 121024"},"PeriodicalIF":4.2,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143307759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operational ozone forecasting system in Mexico City: A machine learning framework integrating forecasted weather and historical ozone data","authors":"Olmo Zavala-Romero ,&nbsp;Pedro A. Segura-Chavez ,&nbsp;Pablo Camacho-Gonzalez ,&nbsp;Jorge Zavala-Hidalgo ,&nbsp;Agustin R. Garcia ,&nbsp;Pavel Oropeza-Alfaro ,&nbsp;Rosario Romero-Centeno ,&nbsp;Octavio Gomez-Ramos","doi":"10.1016/j.atmosenv.2024.121017","DOIUrl":"10.1016/j.atmosenv.2024.121017","url":null,"abstract":"<div><div>Mexico City, a densely populated urban area, experiences multiple episodes of elevated air pollution almost every year. To mitigate the impact of these pollution episodes on the population, it is important to improve forecast systems that allow government authorities to take preventive actions, reducing the exposure of vulnerable groups. This study introduces a pilot operational ozone forecasting system based on machine learning. The proposed system is trained using historical data from a long-standing governmental air quality and atmospheric monitoring network, and with meteorological reanalysis data from a regional implementation of the Weather Research and Forecasting (WRF) model for Mexico. Additional input features are incorporated, including cyclical time encoding for the day of the week, time of day, and day of the year, to improve system accuracy. Once trained, the system utilizes real-time data from multiple atmospheric stations and regional meteorological forecasts to predict ozone levels for the following 24 h. This study evaluates the impact of data augmentation and the advantages of integrating meteorological forecast information into the model. The model achieves a mean absolute error (MAE) of 9.81 ppb and an index of agreement of 0.91 across all stations. For the top 10 stations, the MAE falls below 8.7 ppb, and the index of agreement exceeds 0.93. The system’s performance is comparable to similar systems in other large metropolitan areas and represents an improvement over the existing systems in Mexico City. This operational system is available at <span><span>https://aire.atmosfera.unam.mx/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"344 ","pages":"Article 121017"},"PeriodicalIF":4.2,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143308484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}