Relevant bibliographies by topics / Smog Forecasting

Academic literature on the topic 'Smog Forecasting'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Smog Forecasting"

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Hess, G. D., K. J. Tory, M. E. Cope, S. Lee, K. Puri, P. C. Manins, and M. Young. "The Australian Air Quality Forecasting System. Part II: Case Study of a Sydney 7-Day Photochemical Smog Event." Journal of Applied Meteorology 43, no. 5 (May 1, 2004): 663–79. http://dx.doi.org/10.1175/2094.1.

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Abstract The performance of the Australian Air Quality Forecasting System (AAQFS) is examined by means of a case study of a 7-day photochemical smog event in the Sydney region. This was the worst smog event for the 2000/ 01 oxidant season, and, because of its prolonged nature, it provided the opportunity to demonstrate the ability of AAQFS to forecast situations involving recirculation of precursors and remnant ozone, fumigation, and complex meteorological dynamics. The forecasting system was able to successfully predict high values of ozone, although at times the peak concentrations for the inland stations were underestimated. The dynamics for the Sydney region require a sensitive balance between the synoptic and mesoscale flows. Often high concentrations of ozone were advected inland by the sea breeze. On two occasions the system forecast a synoptic flow that was too strong, which blocked the inland advancement of the sea breeze. The peak ozone forecasts were underpredicted at the inland stations on those occasions. An examination of possible factors causing forecast errors has indicated that the AAQFS is more sensitive to errors in the meteorological conditions, rather than in the emissions or chemical mechanism in the Sydney region.
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Chen, Jiaoyan, Huajun Chen, Zhaohui Wu, Daning Hu, and Jeff Z. Pan. "Forecasting smog-related health hazard based on social media and physical sensor." Information Systems 64 (March 2017): 281–91. http://dx.doi.org/10.1016/j.is.2016.03.011.

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Sipakov, Rostyslav, Olena Voloshkina, Volodimir Trofimovich, and Julia Bereznitska. "IMPACT OF WEATHER FACTORS ON THE SPEED OF THE REACTION OF FORMALDEHYDE FORMATION ABOVE MOTORWAY OVERPASSES." DSpace at USEFUL.academy, no. 2018 (July 2018): 97–102. http://dx.doi.org/10.32557/issn.2640-9631/2018-3.

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The analysis of the actual air condition in the city of Kyiv in the areas of big overpasses and crossroads shows that the average annual concentration of formaldehyde more than 3 times exceeds the maximum permissible concentration (MPC) of this toxic substance. One of the most powerful sources of formaldehyde formation in the air of the city is motor vehicles. The role of weather factors in formaldehyde formation rate (K) depending on capacity of emissions of internal combustion engines has been analyzed in this article. The equation for determining rate constant has been obtained, which depends on the temperature in the city of Kyiv and on the value of effective energy activation of the mentioned process. The comparison of the calculated and measured values of the rate reaction constant in Observation Point (OP) No. 9 in the area of Leningradska square has been given. The conducted research gives the opportunity to assess and obtain forecasting data on atmospheric pollution and probability of smog situations emergence in Kyiv. The suggested methodology can be used for other cities in Ukraine where motor vehicles are the main indicator of photochemical smog emergence.
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Abdul-Wahab, S. A. "IER Photochemical Smog Evaluation and Forecasting of Short-Term Ozone Pollution Levels with Artificial Neural Networks." Process Safety and Environmental Protection 79, no. 2 (March 2001): 117–28. http://dx.doi.org/10.1205/09575820151095201.

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Businger, Steven, Roy Huff, Andre Pattantyus, Keith Horton, A. Jeff Sutton, Tamar Elias, and Tiziana Cherubini. "Observing and Forecasting Vog Dispersion from Kīlauea Volcano, Hawaii." Bulletin of the American Meteorological Society 96, no. 10 (October 1, 2015): 1667–86. http://dx.doi.org/10.1175/bams-d-14-00150.1.

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Abstract Emissions from Kīlauea volcano, known locally as “vog” for volcanic smog, pose significant environmental and health risks to the Hawaiian community. The Vog Measurement and Prediction (VMAP) project was conceived to help mitigate the negative impacts of Kīlauea’s emissions. To date, the VMAP project has achieved the following milestones: i) created a custom application of the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT, hereafter Vog model) to produce statewide forecasts of the concentration and dispersion of sulfur dioxide (SO2) and sulfate aerosol from Kīlauea volcano; ii) developed an ultraviolet (UV) spectrometer array to provide near-real-time volcanic gas emission rate measurements for use as input into the Vog model; iii) developed and deployed a stationary array of ambient SO2 and meteorological sensors to record the spatial characteristics of Kīlauea’s gas plume in high temporal and spatial resolution for model verification; and iv) developed web-based tools to facilitate the dissemination of observations and model forecasts to provide guidance for safety officials and the public, and to raise awareness of the potential hazards of volcanic emissions to respiratory health, agriculture, and general aviation. Wind fields and thermodynamic data from the Weather Research and Forecasting (WRF) Model provide input to the Vog model, with a statewide grid spacing of 3 km and a 1-km grid covering the island of Hawaii. Validation of the Vog model forecasts is accomplished with reference to data from Hawaii State Department of Health ground-based air quality monitors. VMAP results show that this approach can provide useful guidance for the people of Hawaii.
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Holland, Lacey, Steven Businger, Tamar Elias, and Tiziana Cherubini. "Two Ensemble Approaches for Forecasting Sulfur Dioxide Concentrations from Kīlauea Volcano." Weather and Forecasting 35, no. 5 (October 1, 2020): 1923–37. http://dx.doi.org/10.1175/waf-d-19-0189.1.

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AbstractKīlauea volcano, located on the island of Hawaii, is one of the most active volcanoes in the world. It was in a state of nearly continuous eruption from 1983 to 2018 with copious emissions of sulfur dioxide (SO2) that affected public health, agriculture, and infrastructure over large portions of the island. Since 2010, the University of Hawaiʻi at Mānoa provides publicly available vog forecasts that began in 2010 to aid in the mitigation of volcanic smog (or “vog”) as a hazard. In September 2017, the forecast system began to produce operational ensemble forecasts. The months that preceded Kīlauea’s historic lower east rift zone eruption of 2018 provide an opportunity to evaluate the newly implemented air quality ensemble prediction system and compare it another approach to the generation of ensemble members. One of the two approaches generates perturbations in the wind field while the other perturbs the sulfur dioxide (SO2) emission rate from the volcano. This comparison has implications for the limits of forecast predictability under the particularly dynamic conditions at Kīlauea volcano. We show that for ensemble forecasts of SO2 generated under these conditions, the uncertainty associated with the SO2 emission rate approaches that of the uncertainty in the wind field. However, the inclusion of a fluctuating SO2 emission rate has the potential to improve the prediction of the changes in air quality downwind of the volcano with suitable postprocessing.
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Tory, K. J., M. E. Cope, G. D. Hess, S. Lee, K. Puri, P. C. Manins, and N. Wong. "The Australian Air Quality Forecasting System. Part III: Case Study of a Melbourne 4-Day Photochemical Smog Event." Journal of Applied Meteorology 43, no. 5 (May 1, 2004): 680–95. http://dx.doi.org/10.1175/2092.1.

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Abstract A 4-day photochemical smog event in the Melbourne, Victoria, Australia, region (6–9 March 2001) is examined to assess the performance of the Australian Air Quality Forecasting System (AAQFS). Although peak ozone concentrations measured during this period did not exceed the 1-h national air quality standard of 100 ppb, elevated maximum ozone concentrations in the range of 50–80 ppb were recorded at a number of monitoring stations on all four days. These maximum values were in general very well forecast by the AAQFS. On all but the third day the system predicted the advection of ozone precursors over Port Phillip (the adjacent bay) during the morning, where, later in the day, relatively high ozone concentrations developed. The ozone was advected back inland by bay and sea breezes. On the third day, a southerly component to the background wind direction prevented the precursor drainage over the bay, and the characteristic ozone cycle was disrupted. The success of the system's ability to predict peak ozone at individual monitoring stations was largely dependent on the direction and penetration of the sea and bay breezes, which in turn were dependent on the delicate balance between these winds and the opposing synoptic flow.
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Rasid, N., M. Prashnani, J. Goswami, and P. L. N. Raju. "CROP DAMAGE ASSESSMENT IN FLOOD INUNDATED AREA OF MORIGAON DISTRICT OF ASSAM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 489–91. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-489-2019.

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<p><strong>Abstract.</strong> Cropping System Analysis is essential for studying the sustainability of Agriculture. Remote sensing technology provide continuous and synoptic observations of crop area over large extent and substantial contribution in monitoring, evaluating and forecasting of crop and its damage assessment both in cloud and cloud-free environment. Geo-stationary satellites like Synthetic Aperture Radar (SAR) can penetrate through clouds hence it help in assessment of crop even in hazy atmospheric circumstances like fog, smog, light rain, mist etc. The present study reviews cropping pattern and crop rotation of of Morigaon District of Assam. Landsat OLI- 8 multi-spectral data , sentinel 2 multi-spectral and sentinel- 1 SAR data was collected during crop year 2015, 2016 and 2017. The microwave SAR data was used for the classification of crop area for Kharif season due to unavailability of optical cloud free data and also helps in estimation of flood water propagation and its extent and its significant loss to agriculture crop. The result of the pilot study shows that integration of SAR data and GIS environment can be exploited in an efficient way to assess the crop damage area due to flood. Block-wise flood inundation statistics have been derived. This study can be extended to other states/ districts as data collected by satellite can be standardized, the data are reliably objective.</p>
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Foszcz, Dariusz, Tomasz Niedoba, and Jarosław Siewior. "Models of Air Pollution Propagation in the Selected Region of Katowice." Atmosphere 12, no. 6 (May 29, 2021): 695. http://dx.doi.org/10.3390/atmos12060695.

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The paper deals with issues related to analyzing the spread of air pollution and pollutants in large urban agglomerations, specifically, the search for causality between meteorological conditions and the concentrations of particular substances. The pollutants SO2 and PM10 were selected for analysis, which, in addition to NOx, CO, CO2 and PM2.5, contribute to smog, especially during the heating seasons. This analysis is particularly important because Polish environmental standards are more lenient than those in western EU states. Industrial activity, transport and heating systems based on coal-burning are still a big problem in Poland, and each year their gaseous and particulate emissions exceed air-quality limits. This paper presents a statistical analysis of data recorded at the air-quality monitoring station on Kossuth Street in Katowice concerning the heating seasons from 2013–2016. The verification of proposed parabolic models containing concentrations from previous time periods and statistically significant meteorological conditions was conducted for individual heating seasons as well for the whole set of data, which included the influence of wind speed and temperature. The models obtained proved that the selected form of a model is statistically significant, and its use may produce satisfactory forecast results and permit various environmental applications. The specified model might be used both for forecasting (verification and possibly updating coefficients to increase forecast accuracy) and analyzing the factors influencing pollution values. Such statistical analysis may be helpful in assessing the impact of measures adopted to reduce air pollution, particularly in large Polish cities.
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Hu, Jun, Yichen Li, Tianliang Zhao, Jane Liu, Xiao-Ming Hu, Duanyang Liu, Yongcheng Jiang, Jianming Xu, and Luyu Chang. "An important mechanism of regional O<sub>3</sub> transport for summer smog over the Yangtze River Delta in eastern China." Atmospheric Chemistry and Physics 18, no. 22 (November 15, 2018): 16239–51. http://dx.doi.org/10.5194/acp-18-16239-2018.

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Abstract. Severe ozone (O3) pollution episodes plague a few regions in eastern China at certain times of the year, e.g., the Yangtze River Delta (YRD). However, the formation mechanisms, including meteorological factors, contributing to these severe pollution events remain elusive. A severe summer smog stretched over the YRD region from 22 to 25 August 2016. This event displayed hourly surface O3 concentrations that exceeded 300 µg m−3 on 25 August in Nanjing, an urban area in the western YRD. The weather pattern during this period was characterized by near-surface prevailing easterly winds and continuous high air temperatures. The formation mechanism responsible for this O3 pollution episode over the YRD region, particularly the extreme values over the western YRD, was investigated using observation data and by running simulations with the Weather Research and Forecasting model with Chemistry (WRF-Chem). The results showed that the extremely high surface O3 concentration in the western YRD area on 25 August was largely due to regional O3 transport in the nocturnal residual layer (RL) and the diurnal change in the atmospheric boundary layer. On 24 August, high O3 levels, with peak values of 220 µg m−3, occurred in the daytime mixing layer over the eastern YRD region. During nighttime from 24 to 25 August, a shallow stable boundary layer formed near the surface which decoupled the RL above it from the surface. Ozone in the decoupled RL remained quite constant, which resulted in an O3-rich “reservoir” forming in this layer. This reservoir persisted due to the absence of O3 consumption from nitrogen oxide (NO) titration or dry deposition during nighttime. The prevailing easterly winds in the lower troposphere governed the regional transport of this O3-rich air mass in the nocturnal RL from the eastern to the western YRD. As the regional O3 transport reached the RL over the western YRD, O3 concentrations in the RL accumulated and rose to 200 µg m−3 over the western Nanjing site during the sunrise hours on 25 August. The development of the daytime convective boundary layer after sunrise resulted in the disappearance of the RL, as the vertical mixing in the convective boundary layer uniformly redistributed O3 from the upper levels via the entrainment of O3-rich RL air down to the O3-poor air at the ground. This net downward transport flux reached up to 35 µg m−3 h−1, and contributed a considerable surface O3 accumulation, resulting in severe daytime O3 pollution during the summer smog event on 25 August in the western YRD region. The mechanism of regional O3 transport through the nocturnal RL revealed in this study has great implications regarding understanding O3 pollution and air quality change.
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Dissertations / Theses on the topic "Smog Forecasting"

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Tian, Siyuan. "Monitoring and forecasting drought through the assimilation of satellite water observations." Phd thesis, 2018. http://hdl.handle.net/1885/157222.

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Drought poses the greatest threat to freshwater availability and food security, affecting larger areas for longer periods than any other natural hazards. In many regions, droughts increase in frequency and severity due to climate change. As a slow developing natural disaster, better estimates of water availability can be valuable for forecasting droughts and their impacts on ecosystem, agriculture and food security. With accurate knowledge of root-zone soil water and groundwater dynamics, effective planning of water resources and agriculture can be made months in advance. However, the simulated root-zone soil moisture and groundwater are often highly uncertain due to the unpredictable nature of soil water and groundwater dynamics caused by human activities such as water extraction and irrigation. Ground-based and remotely sensed measurements of water content are often limited in both spatial coverage and temporal resolution. Therefore, quantifying the change of water availability and its impacts on vegetation conditions at large scales remains largely unexplored. In my study, contrasting satellite observations of water presence over different vertical domains were assimilated into a global water balance model, providing unprecedented accuracy of soil moisture profile and groundwater storage estimates. The water availability at different depths observed from soil moisture (SMOS) and space gravity (GRACE) missions provides an opportunity to separate total water storage vertically into different layers through data assimilation. However, combining these two data sets is challenging due to the disparity in temporal and spatial resolution at both vertical and horizontal scales. SMOS provides global high spatial and temporal resolution (i.e. 40 km2, 3-day) near-surface (0-5cm) soil moisture estimates from microwave brightness temperature observations. In contrast, the GRACE mission provides accurate measurements of the entire vertically integrated terrestrial water storage column, but it is characterized by low spatial and temporal resolutions (i.e. 300km x 300km, monthly). An ensemble Kalman smoother based global data assimilation system was developed to resolve the discrepancy between model and observations in space and time. The use of data assimilation integrates these two measurements to effectively constrain model simulations and to accurately characterize the vertical distribution of water storage. Compared with model estimates without the assimilation or single-variant assimilation, joint assimilation typically led to more accurate soil moisture profile and groundwater estimates with improved consistency with in situ measurements. The improved water storage estimates integrated over different depths were used to determine the vegetation-accessible storage in association with vegetation growth and surface greenness. Accessible storage reflects a combination of vertical root distribution and soil properties, and its spatial distribution correlates with aridity and vegetation type. Skillful forecasts of vegetation conditions are achievable several months in advance for most of the world's drylands, which offers exciting new prospects for the improvement of drought early warning systems to help reduce human suffering and economical and environmental damage.
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Books on the topic "Smog Forecasting"

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Harley, Robert A., and Dev E. Millstein. Impact of climate change on photochemical air pollution in southern California: Final paper. Sacramento, Calif.]: California Energy Commission, 2009.

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Canada. Government of Canada five-year progress report: Canada-wide standards for particulate matter and ozone. [Ottawa]: Govt. of Canada, 2007.

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Canada. Government of Canada five-year progress report: Canada-wide standards for particulate matter and ozone. Ottawa: Govt. of Canada, 2006.

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Canada. Government of Canada five-year progress report: Canada-wide standards for particulate matter and ozone. [Ottawa]: Govt. of Canada, 2007.

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Book chapters on the topic "Smog Forecasting"

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Wang, Jingmin, and Kanzhang Wu. "Study of the SMO Algorithm Applied in Power System Load Forecasting." In Lecture Notes in Computer Science, 1022–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-36668-3_126.

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Shalini, Pawan Kumar Saini, and Yatendra Mohan Sharma. "An Intelligent Hybrid Model for Forecasting of Heart and Diabetes Diseases with SMO and ANN." In Algorithms for Intelligent Systems, 133–38. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8820-4_13.

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Conference papers on the topic "Smog Forecasting"

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Zhou, Yalin, Jiaoyan Chen, and Huajun Chen. "Observing Social Web for Smog Disaster Forecasting." In WebSci '15: ACM Web Science Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2786451.2786454.

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Chen, Jiaoyan, Huajun Chen, Daning Hu, Jeff Z. Pan, and Yalin Zhou. "Smog disaster forecasting using social web data and physical sensor data." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363850.

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Kisseleva, O. V., E. A. Savelyeva, and I. G. Dadaeva. "FORECASTING THE ECOLOGICAL SITUATION USING NEURAL NETWORKS." In Культура, наука, образование: проблемы и перспективы. Нижневартовский государственный университет, 2021. http://dx.doi.org/10.36906/ksp-2021/73.

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Atmospheric air is a vital component of the natural environment, an integral part of the human, plant, and animal habitat. Ambient air quality is the most important factor affecting health, sanitary and epidemiological situations. With industrial growth, environmental issues and environmental management are revived and take on new significance. To effectively solve these problems, it is necessary to create modern environmental monitoring systems. In this article, we have applied artificial neural networks to predict PM2.5 concentrations as determinants of smog. We used meteorological data and PM2.5 concentrations to create these networks. PM2.5 data and concentrations at several points in the city of Almaty were used as input data for training the model. The measurements were carried out over three months (February-March) from 2019–2021. The best results were shown by a recurrent neural network with long short-term memory, which has proven to be effective in predicting this type of data.
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Wei, Liao, Chen Jianhua, and Gu Yan. "Bed temperature modeling and forecasting of Circulating Fluidized Bed boiler based on SMO algorithm." In 2010 2nd International Conference on Future Computer and Communication. IEEE, 2010. http://dx.doi.org/10.1109/icfcc.2010.5497830.

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Dongxiao Niu and Yongli Wang. "Study of the SMO algorithm based on data mining in shot-term power load forecasting model." In 2008 7th World Congress on Intelligent Control and Automation. IEEE, 2008. http://dx.doi.org/10.1109/wcica.2008.4594026.

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