Journal articles: 'Air – Pollution – Remote sensing'

1

Dubey, Bhawna. "Application of air pollution models and remote sensing in Air Quality Management." Indian Journal of Applied Research 4, no. 5 (October 1, 2011): 266–68. http://dx.doi.org/10.15373/2249555x/may2014/78.

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Benarie, Michel. "Optical remote sensing of air pollution." Science of The Total Environment 44, no. 3 (September 1985): 303–4. http://dx.doi.org/10.1016/0048-9697(85)90107-x.

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WOLF, J. P. "REMOTE SENSING OF AIR POLLUTION BY LIDAR." Le Journal de Physique IV 01, no. C7 (December 1991): C7–13—C7–16. http://dx.doi.org/10.1051/jp4:1991703.

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Liu, Yun, Yuqin Jing, and Yinan Lu. "Research on Quantitative Remote Sensing Monitoring Algorithm of Air Pollution Based on Artificial Intelligence." Journal of Chemistry 2020 (March 4, 2020): 1–7. http://dx.doi.org/10.1155/2020/7390545.

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When the current algorithm is used for quantitative remote sensing monitoring of air pollution, it takes a long time to monitor the air pollution data, and the obtained range coefficient is small. The error between the monitoring result and the actual result is large, and the monitoring efficiency is low, the monitoring range is small, and the monitoring accuracy rate is low. An artificial intelligence-based quantitative monitoring algorithm for air pollution is proposed. The basic theory of atmospheric radiation transmission is analyzed by atmospheric radiation transfer equation, Beer–Bouguer–Lambert law, parallel plane atmospheric radiation theory, atmospheric radiation transmission model, and electromagnetic radiation transmission model. Quantitative remote sensing monitoring of air pollution provides relevant information. The simultaneous equations are constructed on the basis of multiband satellite remote sensing data through pixel information, and the aerosol turbidity of the atmosphere is calculated by the equation decomposition of the pixel information. The quantitative remote sensing monitoring of air pollution is realized according to the calculated aerosol turbidity. The experimental results show that the proposed algorithm has high monitoring efficiency, wide monitoring range, and high monitoring accuracy.

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Trifonova, T. A., N. V. Mishchenko, and Ye P. Grishina. "A REMOTE SENSING-BASED METHOD FOR DETERMINING INDUSTRIAL AIR POLLUTION." Mapping Sciences and Remote Sensing 35, no. 1 (January 1998): 22–30. http://dx.doi.org/10.1080/07493878.1998.10642074.

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Xiong, Xiaozhen, Jane Liu, Liangfu Chen, Weimin Ju, and Fred Moshary. "Special Issue “Remote Sensing of Greenhouse Gases and Air Pollution”." Remote Sensing 13, no. 11 (May 23, 2021): 2057. http://dx.doi.org/10.3390/rs13112057.

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Zelinger, Z., M. Střižı́k, P. Kubát, Z. Jaňour, P. Berger, A. Černý, and P. Engst. "Laser remote sensing and photoacoustic spectrometry applied in air pollution investigation." Optics and Lasers in Engineering 42, no. 4 (October 2004): 403–12. http://dx.doi.org/10.1016/j.optlaseng.2004.03.005.

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Vollmar, H. P., K. Bobey, and M. List. "Compact remote sensing system DIM-measurement of traffic-induced air pollution." Infrared Physics & Technology 37, no. 1 (February 1996): 45–50. http://dx.doi.org/10.1016/1350-4495(95)00110-7.

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Putrenko, V. V., and N. M. Pashynska. "THE USE OF REMOTE SENSING DATA FOR MODELING AIR QUALITY IN THE CITIES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-5/W1 (December 13, 2017): 57–62. http://dx.doi.org/10.5194/isprs-annals-iv-5-w1-57-2017.

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Monitoring of environmental pollution in the cities by the methods of remote sensing of the Earth is actual area of research for sustainable development. Ukraine has a poorly developed network of monitoring stations for air quality, the technical condition of which is deteriorating in recent years. Therefore, the possibility of obtaining data about the condition of air by remote sensing methods is of great importance. The paper considers the possibility of using the data about condition of atmosphere of the project AERONET to assess the air quality in Ukraine. The main pollution indicators were used data on fine particulate matter (PM2.5) and nitrogen dioxide (NO2) content in the atmosphere. The main indicator of air quality in Ukraine is the air pollution index (API). We have built regression models the relationship between indicators of NO2, which are measured by remote sensing methods and ground-based measurements of indicators. There have also been built regression models, the relationship between the data given to the land of NO2 and API. To simulate the relationship between the API and PM2.5 were used geographically weighted regression model, which allows to take into account the territorial differentiation between these indicators. As a result, the maps that show the distribution of the main types of pollution in the territory of Ukraine, were constructed. PM2.5 data modeling is complicated with using existing indicators, which requires a separate organization observation network for PM2.5 content in the atmosphere for sustainable development in cities of Ukraine.

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Misra, P., and W. Takeuchi. "ASSESSING POPULATION SENSITIVITY TO URBAN AIR POLLUTION USING GOOGLE TRENDS AND REMOTE SENSING DATASETS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W11 (February 14, 2020): 93–100. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w11-93-2020.

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Abstract. This study demonstrates relationship between remote sensing satellite retrieved fien aerosol concentration and web-based search volumes of air quality related keywords. People’s perception of urban air pollution can verify policy effectiveness and gauge acceptability of policies. As a serious health issue in Asian cities, population may express concern or uncertainty for air pollution risk by performing search on the web to seek answers. A ‘social sensing’ approach that monitors such search queries, may assess people’ perception about air pollution as a risk. We hypothesize that trend and volume of searches show impact of air pollution on general population. The objectives of this research are to identify those atmospheric conditions under which relative search volume (RSV) obtained from Google Trends shows correlation with measured fine aerosol concentration, and to compare search volume sensitivity to rise in aerosol concentration in seven Asian megacities. We considered weekly relative search volumes from Google Trends (GT) for a four year period from January, 2015 to December, 2018 representing diverse PM2.5 concentrations. Search volumes for keywords corresponding to perception of air quality (‘air pollution’) and health effects (‘cough’ and ‘asthma’) were considered. To represent PM2.5 we used fine aerosol indicator developed in an earlier research. The results suggest that tendency to search for ‘air pollution’ and ‘cough’ occurs when AirRGB R is in excess and temperature is below the baseline values. Consistent with this, in cities with high baseline concentrations, sensitivity to rise in AirRGB R is also comparatively lower. The result of this study can used as an indirect measure of awareness in the form of perception and sensitivity of population to air quality. Such an analysis could be useful for forecasting health risks specially in cities lacking dedicated services.

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Guo, Miaocai. "Application of Remote Sensing Technology in Macro-Ecological Environment Monitoring." Remote Sensing 9, no. 1 (August 12, 2020): 26. http://dx.doi.org/10.18282/rs.v9i1.1099.

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<p>At present, all countries in the world attach great importance to the development and application of remote sensing technology, which is widely used in many fields. By means of detection methods, this technology combines physics knowledge and modern network technology to provide relevant information for human exploration of geology, atmosphere, ocean and weather. In recent years, the global economy has developed rapidly. However, the environmental pollution has become increasingly serious at the same time. Industrial enterprises have discharged a large number of pollutants, resulting in air pollution, water pollution, soil pollution and so on, which seriously endanger human health and life safety. Environmental monitoring is the basis of effective control of environmental pollution. Remote sensing technology can be applied to carry out environmental monitoring and improve the monitoring effect of environmental monitoring.</p>

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Goddijn-Murphy, Lonneke, and Benjamin Williamson. "On Thermal Infrared Remote Sensing of Plastic Pollution in Natural Waters." Remote Sensing 11, no. 18 (September 17, 2019): 2159. http://dx.doi.org/10.3390/rs11182159.

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Plastic pollution in the world’s natural waters is of growing concern and currently receiving significant attention. However, remote sensing of marine plastic litter is still in the developmental stage. Most progress has been made in spectral remote sensing using visible to short-wave infrared wavelengths where optical physics applies. Thermal infrared (TIR) sensing could potentially monitor plastic water pollution but has not been studied in detail. We applied radiative transfer theory to predict TIR sensitivity to changes in the surface fraction of water covered by plastic litter and found that the temperature difference between the water surface and the surroundings controls the TIR signal. Hence, we mapped this difference for various months and times of the day using global SST (sea surface temperature) and t2m (temperature at 2 m height) hourly estimates from the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5. The maps show how SST-t2m difference varied, altering the anticipated effectivity of TIR floating plastic litter remote sensing. We selected several locations of interest to predict the effectivity of TIR sensing of the plastic surface fraction. TIR remote sensing has promising potential and is expected to be more effective in areas with a high air–sea temperature difference.

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Raoufi, Seyyed Sadeq, Hamid Goharnejad, and Mahmoud Zakeri Niri. "Air Pollution Effects on Climate and Air Temperature of Tehran City Using Remote Sensing Data." Asian Journal of Water, Environment and Pollution 15, no. 2 (May 11, 2018): 79–87. http://dx.doi.org/10.3233/ajw-180020.

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Ma, Xin, Chengyi Wang, Ge Han, Yue Ma, Song Li, Wei Gong, and Jialin Chen. "Regional Atmospheric Aerosol Pollution Detection Based on LiDAR Remote Sensing." Remote Sensing 11, no. 20 (October 9, 2019): 2339. http://dx.doi.org/10.3390/rs11202339.

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Atmospheric aerosol is one of the major factors that cause environmental pollution. Light detection and ranging (LiDAR) is an effective remote sensing tool for aerosol observation. In order to provide a comprehensive understanding of the aerosol pollution from the physical perspective, this study investigated regional atmospheric aerosol pollution through the integration of measurements, including LiDAR, satellite, and ground station observations and combined the backward trajectory tracking model. First, the horizontal distribution of atmospheric aerosol wa obtained by a whole-day working scanning micro-pulse LiDAR placed on a residential building roof. Another micro-pulse LiDAR was arranged at a distance from the scanning LiDAR to provide the vertical distribution information of aerosol. A new method combining the slope and Fernald methods was then proposed for the retrieval of the horizontal aerosol extinction coefficient. Finally, whole-day data, including the LiDAR data, the satellite remote sensing data, meteorological data, and backward trajectory tracking model, were selected to reveal the vertical and horizontal distribution characteristics of aerosol pollution and to provide some evidence of the potential pollution sources in the regional area. Results showed that the aerosol pollutants in the district on this specific day were mainly produced locally and distributed below 2.0 km. Six areas with high aerosol concentration were detected in the scanning area, showing that the aerosol pollution was mainly obtained from local life, transportation, and industrial activities. Correlation analysis with the particulate matter data of the ground air quality national control station verified the accuracy of the LiDAR detection results and revealed the effectiveness of LiDAR detection of atmospheric aerosol pollution.

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Song, Wen, Wei Song, Haihong Gu, and Fuping Li. "Progress in the Remote Sensing Monitoring of the Ecological Environment in Mining Areas." International Journal of Environmental Research and Public Health 17, no. 6 (March 12, 2020): 1846. http://dx.doi.org/10.3390/ijerph17061846.

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Based on the results of an extensive literature research, we summarize the research progress of remote sensing monitoring in terms of identifying mining area boundaries and monitoring land use or land cover changes of mining areas. We also analyze the application of remote sensing in monitoring the biodiversity, landscape structure, vegetation change, soil environment, surface runoff conditions, and the atmospheric environment in mining areas and predict the prospects of remote sensing in monitoring the ecological environment in mining areas. Based on the results, the accurate classification of land use or land cover and the accurate extraction of environmental factors are the basis for remote sensing monitoring of the ecological environment in mining areas. In terms of the extraction of ecological factors, vegetation extraction is relatively advanced in contrast to the extraction of animal and microbial data. For the monitoring of environmental conditions of mining areas, sophisticated methods are available to identify pollution levels of vegetation and to accurately monitor soil quality. However, the methods for water and air pollution monitoring in mining areas still need to be improved. These limitations considerably impede the application of remote sensing monitoring in mining areas. The solving of these problems depends on the progress of multi-source remote sensing data and stereoscopic monitoring techniques.

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Wu, Rui, Honglei Xu, Jie Liu, Xiaowen Yang, Xiaoyu Tan, Weiwei Gong, and Jing Lin. "Layout Methods of Monitoring Stations for Diesel Freight Trucks Emission Supervision Using Highway Traffic Survey Data—— Taking Shanxi Province as an Example." E3S Web of Conferences 145 (2020): 02026. http://dx.doi.org/10.1051/e3sconf/202014502026.

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The in-use diesel trucks have high use intensity, high mobility, and frequent excessive emissions, which have become one of the important air pollution sources. The Action Plan for Tackling the Challenges of Diesel Truck Pollution Control issued by the Chinese government requires the establishment of a national monitoring network for transport-related air pollution and the use of various means to monitor diesel truck emissions. This study briefly summarized the requirements for diesel truck emission monitoring and supervision. Relying on the highway network traffic survey data, a method for identifying the main route of regional highway freight transportation was proposed. Then referring to the experience of highway air quality monitoring networks at home and abroad, the article proposed layout methods for roadside air quality monitoring stations, vehicle remote sensing monitoring stations, and road inspection stations, which fills the gap in domestic methods. At last, a demonstration study on the layout plan for the in-use diesel truck emissions monitoring stations in Shanxi Province was carried out. Taking Shanxi’s main highway freight corridors as the key supervision area, this article screened out 44 roadside air quality monitoring stations, 24 vehicle remote sensing monitoring stations, and 15 road inspection stations.

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Brauer, Michael, Randall V. Martin, Aaron van Donkelaar, Dan Crouse, Sarah B. Henderson, Jiayun Yao, Richard T. Burnett, and Perry Hystad. "Remote sensing approaches to estimate air pollution exposure for disease burden and epidemiology." ISEE Conference Abstracts 2013, no. 1 (September 19, 2013): 5713. http://dx.doi.org/10.1289/isee.2013.s-4-13-04.

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Jung, Hyung-Sup, Saro Lee, and Biswajeet Pradhan. "Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations." Sustainability 12, no. 6 (March 19, 2020): 2390. http://dx.doi.org/10.3390/su12062390.

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The Special Issue on “Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations” is published. A total of 20 qualified papers are published in this Special Issue. The topics of the papers are the application of remote sensing and geospatial information systems to Earth observations in various fields such as (1) object change detection, (2) air pollution, (3) earthquakes, (4) landslides, (5) mining, (6) biomass, (7) groundwater, and (8) urban development using the techniques of remote sensing and geospatial information systems. More than 100 researchers have participated in this Special Issue. We hope that this Special Issue is helpful for sustainable applications.

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Tian, X. M., D. Liu, S. L. Fu, D. C. Wu, B. X. Wang, Z. Wang, and Y. Wang. "CHARACTERIZATION OF SPRING AIR POLLUTION OF BEIJING IN 2019 USING ACTIVE AND PASSIVE REMOTE SENSING INSTRUMENTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W9 (October 25, 2019): 153–58. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w9-153-2019.

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Abstract. As the capital of China, the Beijing area needs to be paid special attention to its air quality. We used active remote sensing instrument (ground-based lidar), combined with passive remote sensing instrument (VIIRS onboard the NPP spacecraft), to study the serious pollution event over Beijing in spring of 2019. At the same time, the ground-based particulate matter (PM) data and the meteorological element data were analyzed. It is found that the ratio of concentrations of PM2.5 to PM10 is very large during the pollution period. The mean value of ratio is 0.75464, indicated it is fine particulate matter pollution. The Range correction signal (RCS) of lidar is very large in the layer below 0.5 km. But the volume depolarization ratio (VDR) is much less than 0.05. It indicated it is anthropogenic urban aerosols. The change in the aerosol optical depth (AOD) of VIIRS during pollution is consistent with the change in optical properties observed by lidar. The backward trajectory model of HYSPLIT shows that the pollutant came from the Hebei area where industrial pollution is serious, and the local meteorological conditions in Beijing are conducive to the continuous accumulation of pollutants. This work can deepen the understanding of the mechanism of haze formation and can help and support pollution prevention work.

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Schriel, R. C. "OPERATIONAL AIR SURVEILLANCE AND EXPERIENCES IN THE NETHERLANDS." International Oil Spill Conference Proceedings 1987, no. 1 (April 1, 1987): 129–36. http://dx.doi.org/10.7901/2169-3358-1987-1-129.

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ABSTRACT Surveillance of suspected oil pollution incidents in the Dutch sector of the North Sea by aircraft has proven useful in reducing the amount of oil discharged and apprehension of violaters. An operational approach based on experience has been developed, using both visual and remote sensing observations. A number of practical suggestions have been put forward for use in establishing whether or not a violation has occurred.

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Sidman, Gabriel, Sydney Fuhrig, and Geeta Batra. "The Use of Remote Sensing Analysis for Evaluating the Impact of Development Projects in the Yellow Sea Large Marine Ecosystem." Sustainability 12, no. 9 (April 30, 2020): 3628. http://dx.doi.org/10.3390/su12093628.

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Remote sensing has long been valued as a data source for monitoring environmental indicators and detecting trends in ecosystem stress from anthropogenic causes such as deforestation, river dams and air and water pollution. More recently, remote sensing analyses have been applied to evaluate the impacts of environmental projects and programs on reducing environmental stresses. Such evaluation has focused primarily on the change in above-surface vegetation such as forests. This study uses remote sensing ocean color products to evaluate the impact on reducing marine pollution of the Global Environment Facility’s (GEF) portfolio of projects in the Yellow Sea Large Marine Ecosystem. Chlorophyll concentration was derived from satellite images over a time series from the 1990s, when GEF projects began, until the present. Results show a 50% increase in chlorophyll until 2011 followed by a 34% decrease until 2019, showing a potential delayed effect of pollution control efforts. The rich time series data is a major advantage to using geospatial analysis for evaluating the impacts of environmental interventions on marine pollution. However, one drawback to the method is that it provides insights into correlations but cannot attribute the results to any particular cause, such as GEF interventions.

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Fernández-Pacheco, V. M., C. A. López-Sánchez, E. Álvarez-Álvarez, M. J. Suárez López, L. García-Expósito, E. Antuña Yudego, and J. L. Carús-Candás. "Estimation of PM10 Distribution using Landsat5 and Landsat8 Remote Sensing." Proceedings 2, no. 23 (October 31, 2018): 1430. http://dx.doi.org/10.3390/proceedings2231430.

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Air pollution is one of the major environmental problems, especially in industrial and highly populated areas. Remote sensing image is a rich source of information with many uses. This paper is focused on estimation of air pollutants using Landsat-5 TM and Landsat-8 OLI satellite images. Particulate Matter with particle size less than 10 microns (PM10) is estimated for the study area of Principado de Asturias (Spain). When a satellite records the radiance of the surface received at sensor, does not represent the true radiance of the surface. A noise caused by Aerosol and Particulate Matters attenuate that radiance. In many applications of remote sensing, that noise called path radiance is removed during pre-processing. Instead, path radiance was used to estimate the PM10 concentration in the air. A relationship between the path radiance and PM10 measurements from ground stations has been established using Random Forest (RF) algorithm and a PM10 map was generated for the study area. The results show that PM10 estimation through satellite image is an efficient technique and it is suitable for local and regional studies.

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Joshi, Jyotsana, Kishor Chandra Kandpal, and Neelam Rawat. "Estimation of Air Pollution Using Multi-Temporal Remote Sensing Technique for Dehradun District, Uttarakhand." International Journal of Advanced Remote Sensing and GIS 8, no. 1 (January 16, 2019): 2919–32. http://dx.doi.org/10.23953/cloud.ijarsg.400.

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Emeis, Stefan, and Klaus Schäfer. "Remote Sensing Methods to Investigate Boundary-layer Structures relevant to Air Pollution in Cities." Boundary-Layer Meteorology 121, no. 2 (June 22, 2006): 377–85. http://dx.doi.org/10.1007/s10546-006-9068-2.

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Ke, Luong Chinh, Ho Thi Van Trang, Vu Huu Liem, Tran Ngoc Tuong, and Pham Thi Duyen. "Assessment of surface water pollutant models of estuaries and coastal zone of Quang Ninh – Hai Phong using Spot-5 images." Geodesy and Cartography 64, no. 1 (June 1, 2015): 29–42. http://dx.doi.org/10.1515/geocart-2015-0004.

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Abstract The coastal zone and estuaries of Quang Ninh and Hai Phong have great potential not only for economic development but also for protection and conservation of biodiversity and ecosystem. Nowadays, due to industrial, agricultural and anthropogenic activities signs of water pollution in the region have been found. The level of surface water pollution can be determined by traditional methods through observatory stations. However, a traditional approach to determine water contamination is discontinuous, and thereby makes pollution assessment of the entire estuary very difficult. Nowadays, remote sensing technology has been developed and widely applied in many fields, for instance, in monitoring water environments. Remote sensing data combined with information from in-situ observations allow for extraction of polluted components in water and accurate measurements of pollution level in the large regions ensuring objectivity. According to results obtained from Spot-5 imagery of Quang Ninh and Hai Phong, the extracted pollution components, like BOD, COD and TSS can be determined with the root mean square error, the absolute mean error and the absolute mean percentage error (%): ±4.37 (mg/l) 3.86 (mg/l), 27%; ±55.32 (mg/l), 48.30 (mg/l), 14%; and ±32.90 (mg/l), 23.38 (mg/l), 28%; respectively. Obtained outcomes guarantee objectivity in assessing water contaminant levels in the investigated regions and show the advantages of remote sensing applications in Resource and Environmental Monitoring in relation to Water – Air – Land.

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Kocifaj, Miroslav, and Salvador Bará. "Aerosol characterization using satellite remote sensing of light pollution sources at night." Monthly Notices of the Royal Astronomical Society: Letters 495, no. 1 (April 7, 2020): L76—L80. http://dx.doi.org/10.1093/mnrasl/slaa060.

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ABSTRACT A demanding challenge in atmospheric research is the night-time characterization of aerosols using passive techniques, that is, by extracting information from scattered light that has not been emitted by the observer. Satellite observations of artificial night-time lights have been used to retrieve some basic integral parameters, like the aerosol optical depth. However, a thorough analysis of the scattering processes allows one to obtain substantially more detailed information on aerosol properties. In this letter, we demonstrate a practicable approach for determining the aerosol particle size number distribution function in the air column, based on the measurement of the angular radiance distribution of the scattered light emitted by night-time lights of cities and towns, recorded from low Earth orbit. The method is self-calibrating and does not require the knowledge of the absolute city emissions. The input radiance data are readily available from several spaceborne platforms, like the VIIRS-DNB radiometer onboard the Suomi-NPP satellite.

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Semlali, Bad-reddine Boudriki, Chaker El Amrani, and Siegfried Denys. "Development of a Java-based application for environmental remote sensing data processing." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (June 1, 2019): 1978. http://dx.doi.org/10.11591/ijece.v9i3.pp1978-1986.

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Air pollution is one of the most serious problems the world faces today. It is highly necessary to monitor pollutants in real-time to anticipate and reduce damages caused in several fields of activities. Likewise, it is necessary to provide decision makers with useful and updated environmental data. As a solution to a part of the above-mentioned necessities, we developed a Java-based application software to collect, process and visualize several environmental and pollution data, acquired from the Mediterranean Dialog earth Observatory (MDEO) platform [1]. This application will amass data of Morocco area from EUMETSAT satellites, and will decompress, filter and classify the received datasets. Then we will use the processed data to build an interactive environmental real-time map of Morocco. This should help finding out potential correlations between pollutants and emitting sources.

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Corno, Fulvio, Teodoro Montanaro, Carmelo Migliore, and Pino Castrogiovanni. "SmartBike: an IoT Crowd Sensing Platform for Monitoring City Air Pollution." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (December 1, 2017): 3602. http://dx.doi.org/10.11591/ijece.v7i6.pp3602-3612.

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<p>In recent years, the Smart City concept is emerging as a way to increase efficiency, reduce costs, and improve the overall quality of citizen life. The rise of Smart City solutions is encouraged by the increasing availability of Internet of Things (IoT) devices and crowd sensing technologies. This paper presents an IoT Crowd Sensing platform that offers a set of services to citizens by exploiting a network of bicycles as IoT probes. Based on a survey conducted to identify the most interesting bike-enabled services, the SmartBike platform provides: real time remote geo-location of users’ bikes, anti-theft service, information about traveled route, and air pollution monitoring. The proposed SmartBike platform is composed of three main components: the SmartBike mobile sensors for data collection installed on the bicycle; the end-user devices implementing the user interface for geo-location and anti-theft; and the SmartBike central servers for storing and processing detected data and providing a web interface for data visualization. The suitability of the platform was evaluated through the implementation of an initial prototype. Results demonstrate that the proposed SmartBike platform is able to provide the stated services, and, in addition, that the accuracy of the acquired air quality measurements is compatible with the one provided by the official environmental monitoring system of the city of Turin. The described platform will be adopted within a project promoted by the city of Turin, that aims at helping people making their mobility behavior more sustainable.</p>

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Tran, Van Thi, Binh Thi Trinh, and Bao Duong Xuan Ha. "STUDY OF DUST POLLUTION DETECTING ABILITY IN URBAN AREAS BY REMOTE SENSING TECHNOLOGY TO SUPPORT AIR ENVIRONMENT OBSERVATION." Science and Technology Development Journal 15, no. 4 (December 30, 2012): 33–47. http://dx.doi.org/10.32508/stdj.v15i4.1822.

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This paper presents the approach towards application of remote sensing technology to monitor the air environemnt. Specific inital research is findings PM10 dust from SPOT 5 satellite image. The calculation based on reflectance value on remote sensing satellite images. The main method is to calculate statistical correlation regression between the PM10 concentration from ground station observations and reflectance value on each image band and the main components of satellite imagery in 2003 to find the best regression function, applied then to images 2011 where its radiance value was relatively normalized under atmospheric, geometric, environmental conditions of image 2003. The results showed the best correlation in nonlinear regression case. Spatial distribution of PM10 concentrations > 200μg/m3 found on most main roads, industrial parks and residential areas. This study is a first step test, but the results have demonstrated that satellite imagery can be used as a useful, effective tool, to monitor air environment in cities.

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Belic, Ilija, Ljubinka Radosavljevic, Miroljub Milincic, and Dejan Sabic. "Laser system for remote sensing monitoring of air pollution and quality control of the atmosphere." Thermal Science 16, no. 4 (2012): 1201–11. http://dx.doi.org/10.2298/tsci120226136b.

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Hsu, Chin-Yu, Chih-Da Wu, Ya-Ping Hsiao, Yu-Cheng Chen, Mu-Jean Chen, and Shih-Chun Lung. "Developing Land-Use Regression Models to Estimate PM2.5-Bound Compound Concentrations." Remote Sensing 10, no. 12 (December 6, 2018): 1971. http://dx.doi.org/10.3390/rs10121971.

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Epidemiology estimates how exposure to pollutants may impact human health. It often needs detailed determination of ambient concentrations to avoid exposure misclassification. However, it is unrealistic to collect pollutant data from each and every subject. Land-use regression (LUR) models have thus been used frequently to estimate individual levels of exposures to ambient air pollution. This paper used remote sensing and geographical information system (GIS) tools to develop ten regression models for PM2.5-bound compound concentration based on measurements of a six-year period including , OC, EC, Ba, Mn, Cu, Zn, and Sb. The explained variance (R2) of these LUR models ranging from 0.60 to 0.92 confirms that this study successfully estimated the fine spatial variability of PM2.5-bound compound concentrations in Taiwan where the distribution of traffic, industrial area, greenness, and culture-specific PM2.5 sources like temples collected from GIS and remote sensing data were main variables. In particular, while they were much less used, this study showcased the necessity of remote sensing data of greenness in future LUR studies for reducing the exposure bias. In terms of local residents’ health outcome or health effect indicators, this study further offers much-needed support for future air epidemiological studies. The results provide important insights into expanding the application of GIS and remote sensing on exposure assessment for PM2.5-bound compounds.

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Thi Van, Tran, Nguyen Hang Hai, Vo Quoc Bao, and Ha Duong Xuan Bao. "Remote Sensing-Based Aerosol Optical Thickness for Monitoring Particular Matter over the City." Proceedings 2, no. 7 (March 22, 2018): 362. http://dx.doi.org/10.3390/ecrs-2-05175.

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Urban development contributing to air pollution is one of the factors seriously affecting public health. Besides the traditional ground observation methods, the current space technology has been added to the monitoring and managing environment. This research used Landsat satellite image to detect PM10 from by the Aerosol Optical Thickness (AOT) method for Ho Chi Minh City area. The regression analysis was used for establishing the relationship between the PM10 data obtained at ground stations and AOT values from processed images in 2003. The analysis showed a good correlation coefficient (R = 0.95) for the case of AOT calculated from spectral reflective green band. The relative radiation normalization was carried out for satellite imaging in 2015 in order to simulate the spatial distribution of PM10 with the same regression function. The distribution for PM10 aerosol pollution is focused on the urban area, traffic booth and industrial zones. The results of this study provided a picture of general distribution for current pollution status and also supported the determining of specified polluted areas. This has provided helpful and good support for zoning and urban environmental management in accordance with urban development.

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Owais, Mahmoud. "Location Strategy for Traffic Emission Remote Sensing Monitors to Capture the Violated Emissions." Journal of Advanced Transportation 2019 (March 18, 2019): 1–9. http://dx.doi.org/10.1155/2019/6520818.

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Air contamination becomes an urgent problem to be considered as a result of the rapid growth in traffic all over the world. Traffic emissions differ from vehicle to vehicle depending on the vehicle type, production year, fuel octane number, and periodical maintenance of the vehicle. The majority of drivers do not revise their harmful vehicles emissions regularly. Therefore, effective tracking of high-emitting vehicles can be an important solution for reducing traffic air pollution. This study proposes a location strategy for vehicle remote sensing monitors aided with ID-plate recognizer to capture any violated vehicle emissions. The problem is formulated into a graph theory problem, and then a novel adapted metaheuristic algorithm is used to solve the problem. The methodology, using a benchmark problem, has managed to solve the problem to the optimality. Moreover, its robustness is measured statistically.

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Demetillo, Mary Angelique G., Aracely Navarro, Katherine K. Knowles, Kimberly P. Fields, Jeffrey A. Geddes, Caroline R. Nowlan, Scott J. Janz, et al. "Observing Nitrogen Dioxide Air Pollution Inequality Using High-Spatial-Resolution Remote Sensing Measurements in Houston, Texas." Environmental Science & Technology 54, no. 16 (August 5, 2020): 9882–95. http://dx.doi.org/10.1021/acs.est.0c01864.

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Palve, S. N., P. D. Nemade, and S. D. Ghude. "The application of remote sensing techniques for air pollution analysis and climate change on Indian subcontinent." IOP Conference Series: Earth and Environmental Science 37 (June 2016): 012076. http://dx.doi.org/10.1088/1755-1315/37/1/012076.

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Wang, Junde, Jianxia Kang, Zuoru Chen, Mei Huang, Jupei Zhang, and Tlanshu Wang. "The study of freon‐12 in alcohol / air flame with remote sensing fourier transform infrared emission spectroscopy∗." Journal of Environmental Science and Health . Part A: Environmental Science and Engineering and Toxicology 30, no. 10 (December 1995): 2111–22. http://dx.doi.org/10.1080/10934529509376327.

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Kaplan, Gordana, Zehra Yigit Avdan, and Ugur Avdan. "Spaceborne Nitrogen Dioxide Observations from the Sentinel-5P TROPOMI over Turkey." Proceedings 18, no. 1 (May 23, 2019): 4. http://dx.doi.org/10.3390/ecrs-3-06181.

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With rapid population growth, both urbanization and transportation affect air pollution, population health, and global warming. A number of air pollutants are released from industrial facilities and other activities and may cause adverse effects on human health and the environment. One of the biggest air pollutants, nitrogen dioxide (NO2), is mainly caused by the combustion of fossil fuels, especially from traffic exhaust gases. Over the years, air pollution has been monitored using satellite remote sensing data. In this study, we investigate the relationship of the tropospheric NO2 retrieved from the recently launched Sentinel-5 Precursor, a low-earth-orbit atmosphere mission dedicated to monitoring air pollution equipped with the spectrometer Tropomoi (Tropospheric Monitoring Instrument), and the population density over Turkey. For this purpose, we use the mean value of the NO2 collected from July 2018 to January 2019 and the statistic population data from 2017. The results showed a significant correlation of higher than 0.72 between the population density and the maximum NO2 values. For future studies, we recommend investigating the correlation of different air pollutants with population and other factors contributing to air and environmental pollution.

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Elshorbany, Yasin F., Hannah C. Kapper, Jerald R. Ziemke, and Scott A. Parr. "The Status of Air Quality in the United States During the COVID-19 Pandemic: A Remote Sensing Perspective." Remote Sensing 13, no. 3 (January 21, 2021): 369. http://dx.doi.org/10.3390/rs13030369.

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The recent COVID-19 pandemic has prompted global governments to take several measures to limit and contain the spread of the novel virus. In the United States (US), most states have imposed a partial to complete lockdown that has led to decreased traffic volumes and reduced vehicle emissions. In this study, we investigate the impacts of the pandemic-related lockdown on air quality in the US using remote sensing products for nitrogen dioxide tropospheric column (NO2), carbon monoxide atmospheric column (CO), tropospheric ozone column (O3), and aerosol optical depth (AOD). We focus on states with distinctive anomalies and high traffic volume, New York (NY), Illinois (IL), Florida (FL), Texas (TX), and California (CA). We evaluate the effectiveness of reduced traffic volume to improve air quality by comparing the significant reductions during the pandemic to the interannual variability (IAV) of a respective reference period for each pollutant. We also investigate and address the potential factors that might have contributed to changes in air quality during the pandemic. As a result of the lockdown and the significant reduction in traffic volume, there have been reductions in CO and NO2. These reductions were, in many instances, compensated by local emissions and, or affected by meteorological conditions. Ozone was reduced by varying magnitude in all cases related to the decrease or increase of NO2 concentrations, depending on ozone photochemical sensitivity. Regarding the policy impacts of this large-scale experiment, our results indicate that reduction of traffic volume during the pandemic was effective in improving air quality in regions where traffic is the main pollution source, such as in New York City and FL, while was not effective in reducing pollution events where other pollution sources dominate, such as in IL, TX and CA. Therefore, policies to reduce other emissions sources (e.g., industrial emissions) should also be considered, especially in places where the reduction in traffic volume was not effective in improving air quality (AQ).

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Sudhakar, P., P. Kalavathi, D. Ramakrishna Rao, and M. Satyanarayna. "Design of Laser Based Monitoring Systems for Compliance Management of Odorous and Hazardous Air Pollutants in Selected Chemical Industrial Estates at Hyderabad, India." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (December 23, 2014): 1467–70. http://dx.doi.org/10.5194/isprsarchives-xl-8-1467-2014.

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Industrialization can no longer sustain without internalization of the concerns of the receiving environment and land-use. Increased awareness and public pressure, coupled with regulatory instruments and bodies exert constant pressure on industries to control their emissions to a level acceptable to the receiving environment. However, when a group of industries come-up together as an industrial estate, the cumulative impacts of all the industries together often challenges the expected/desired quality of receiving environment, requiring stringent pollution control and monitoring measures. Laser remote sensing techniques provide powerful tools for environmental monitoring. These methods provide range resolved measurements of concentrations of various gaseous pollutants and suspended particulate matter (SPM) not only in the path of the beam but over the entire area. A three dimensional mapping of the pollutants and their dispersal can be estimated using the laser remote sensing methods on a continuous basis. Laser Radar (Lidar) systems are the measurements technology used in the laser remote sensing methods. Differential absorption lidar (DIAL) and Raman Lidar technologies have proved to be very useful for remote sensing of air pollutants. DIAL and Raman lidar systems can be applied for range resolved measurements of molecules like SO2, NO2, O3 Hg, CO, C2H4, H2O, CH4, hydrocarbons etc. in real time on a continuous basis. This paper describes the design details of the DAIL and Raman lidar techniques for measurement of various hazardous air pollutants which are being released into the atmosphere by the chemical industries operating in the Bachupally industrial Estate area at Hyderabad, India. The relative merits of the two techniques have been studied and the minimum concentration of pollutants that can be measured using these systems are presented. A dispersion model of the air pollutants in the selected chemical industrial estates at Hyderabad has been developed.

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Zhang, Haopeng, and Qin Deng. "Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study." Remote Sensing 11, no. 9 (May 10, 2019): 1117. http://dx.doi.org/10.3390/rs11091117.

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The frequent hazy weather with air pollution in North China has aroused wide attention in the past few years. One of the most important pollution resource is the anthropogenic emission by fossil-fuel power plants. To relieve the pollution and assist urban environment monitoring, it is necessary to continuously monitor the working status of power plants. Satellite or airborne remote sensing provides high quality data for such tasks. In this paper, we design a power plant monitoring framework based on deep learning to automatically detect the power plants and determine their working status in high resolution remote sensing images (RSIs). To this end, we collected a dataset named BUAA-FFPP60 containing RSIs of over 60 fossil-fuel power plants in the Beijing-Tianjin-Hebei region in North China, which covers about 123 km 2 of an urban area. We compared eight state-of-the-art deep learning models and comprehensively analyzed their performance on accuracy, speed, and hardware cost. Experimental results illustrate that our deep learning based framework can effectively detect the fossil-fuel power plants and determine their working status with mean average precision up to 0.8273, showing good potential for urban environment monitoring.

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Panahifar, Hossein, and Hamid Khalesifard. "Tracking atmospheric boundary layer in tehran using combined lidar remote sensing and ground base measurements." EPJ Web of Conferences 176 (2018): 06011. http://dx.doi.org/10.1051/epjconf/201817606011.

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The vertical structure of the atmospheric boundary layer (ABL) has been studied by use of a depolarized LiDAR over Tehran, Iran. The boundary layer height (BLH) remains under 1km, and its retrieval from LiDAR have been compared with sonding measurements and meteorological model outputs. It is also shown that the wind speed and direction as well as topography lead to the persistence of air pollution in Tehran. The situation aggravate in fall and winter due to temperature inversion.

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Sullivan, John T., Timothy Berkoff, Guillaume Gronoff, Travis Knepp, Margaret Pippin, Danette Allen, Laurence Twigg, et al. "The Ozone Water–Land Environmental Transition Study: An Innovative Strategy for Understanding Chesapeake Bay Pollution Events." Bulletin of the American Meteorological Society 100, no. 2 (February 2019): 291–306. http://dx.doi.org/10.1175/bams-d-18-0025.1.

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AbstractCoastal regions have historically represented a significant challenge for air quality investigations because of water–land boundary transition characteristics and a paucity of measurements available over water. Prior studies have identified the formation of high levels of ozone over water bodies, such as the Chesapeake Bay, that can potentially recirculate back over land to significantly impact populated areas. Earth-observing satellites and forecast models face challenges in capturing the coastal transition zone where small-scale meteorological dynamics are complex and large changes in pollutants can occur on very short spatial and temporal scales. An observation strategy is presented to synchronously measure pollutants “over land” and “over water” to provide a more complete picture of chemical gradients across coastal boundaries for both the needs of state and local environmental management and new remote sensing platforms. Intensive vertical profile information from ozone lidar systems and ozonesondes, obtained at two main sites, one over land and the other over water, are complemented by remote sensing and in situ observations of air quality from ground-based, airborne (both personned and unpersonned), and shipborne platforms. These observations, coupled with reliable chemical transport simulations, such as the National Oceanic and Atmospheric Administration (NOAA) National Air Quality Forecast Capability (NAQFC), are expected to lead to a more fully characterized and complete land–water interaction observing system that can be used to assess future geostationary air quality instruments, such as the National Aeronautics and Space Administration (NASA) Tropospheric Emissions: Monitoring of Pollution (TEMPO), and current low-Earth-orbiting satellites, such as the European Space Agency’s Sentinel-5 Precursor (S5-P) with its Tropospheric Monitoring Instrument (TROPOMI).

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Mahajan, Sachit, Jennifer Gabrys, and Joanne Armitage. "AirKit: A Citizen-Sensing Toolkit for Monitoring Air Quality." Sensors 21, no. 12 (June 11, 2021): 4044. http://dx.doi.org/10.3390/s21124044.

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Increasing urbanisation and a better understanding of the negative health effects of air pollution have accelerated the use of Internet of Things (IoT)-based air quality sensors. Low-cost and low-power sensors are now readily available and commonly deployed by individuals and community groups. However, there are a wide range of such IoT devices in circulation that differently focus on problems of sensor validation, data reliability, or accessibility. In this paper, we present AirKit, which was developed as an integrated and open source “social IoT technology”. AirKit enables a comprehensive approach to citizen-sensing air quality through several integrated components: (1) the Dustbox 2.0, a particulate matter sensor; (2) Airsift, a data analysis platform; (3) a reliable and automatic remote firmware update system; (4) a “Data Stories” method and tool for communicating citizen data; and (5) an AirKit logbook that provides a guide for designing and running air quality projects, along with instructions for building and using AirKit components. Developed as a social technology toolkit to foster open processes of research co-creation and environmental action, Airkit has the potential to generate expanded engagements with IoT and air quality by improving the accuracy, legibility and use of sensors, data analysis and data communication.

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Chen, Chien-Yuan, Ho Wen Chen, Chu-Ting Sun, Yen Hsun Chuang, Kieu Lan Phuong Nguyen, and Yu Ting Lin. "Impact assessment of river dust on regional air quality through integrated remote sensing and air quality modeling." Science of The Total Environment 755 (February 2021): 142621. http://dx.doi.org/10.1016/j.scitotenv.2020.142621.

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Zhou, Chunshan, Shijie Li, and Shaojian Wang. "Examining the Impacts of Urban Form on Air Pollution in Developing Countries: A Case Study of China’s Megacities." International Journal of Environmental Research and Public Health 15, no. 8 (July 24, 2018): 1565. http://dx.doi.org/10.3390/ijerph15081565.

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Urban form is increasingly being identified as an important determinant of air pollution in developed countries. However, the effect of urban form on air pollution in developing countries has not been adequately addressed in the literature to date, which points to an evident omission in current literature. In order to fill this gap, this study was designed to estimate the impacts of urban form on air pollution for a panel made up of China’s five most rapidly developing megacities (Beijing, Tianjin, Shanghai, Chongqing, and Guangzhou) using time series data from 2000 to 2012. Using the official Air Pollution Index (API) data, this study developed three quantitative indicators: mean air pollution index (MAPI), air pollution ratio (APR), and continuous air pollution ratio (CAPR), to evaluate air pollution levels. Moreover, seven landscape metrics were calculated for the assessment of urban form based on three aspects (urban size, urban shape irregularity, and urban fragmentation) using remote sensing data. Panel data models were subsequently employed to quantify the links between urban form and air pollution. The empirical results demonstrate that urban expansion surprisingly helps to reduce air pollution. The substitution of clean energy for dirty energy that results from urbanization in China offers a possible explanation for this finding. Furthermore, urban shape irregularity positively correlated with the number of days with polluted air conditions, a result could be explained in terms of the influence of urban geometry on traffic congestion in Chinese cities. In addition, a negative association was identified between urban fragmentation and the number of continuous days of air pollution, indicating that polycentric urban forms should be adopted in order to shorten continuous pollution processes. If serious about achieving the meaningful alleviation of air pollution, decision makers and urban planners should take urban form into account when developing sustainable cities in developing countries like China.

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Pahlevan, Nima, Zhongping Lee, Chuanmin Hu, and John R. Schott. "Diurnal remote sensing of coastal/oceanic waters: a radiometric analysis for Geostationary Coastal and Air Pollution Events." Applied Optics 53, no. 4 (January 28, 2014): 648. http://dx.doi.org/10.1364/ao.53.000648.

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Roots, Ott Otto, Antti Roose, and Kalju Eerme. "Remote sensing of climate change, long-term monitoring of air pollution and stone material corrosion in Estonia." International Journal of Remote Sensing 32, no. 24 (August 22, 2011): 9691–705. http://dx.doi.org/10.1080/01431161.2011.574163.

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Wang, Yanjun, Mengjie Wang, Bo Huang, Shaochun Li, and Yunhao Lin. "Estimation and Analysis of the Nighttime PM2.5 Concentration Based on LJ1-01 Images: A Case Study in the Pearl River Delta Urban Agglomeration of China." Remote Sensing 13, no. 17 (August 27, 2021): 3405. http://dx.doi.org/10.3390/rs13173405.

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At present, fine particulate matter (PM2.5) has become an important pollutant in regard to air pollution and has seriously harmed the ecological environment and human health. In the face of increasingly serious PM2.5 air pollution problems, feasible large-scale continuous spatial PM2.5 concentration monitoring provides great practical value and potential. Based on radiative transfer theory, a correlation model of the nighttime light radiance and ground PM2.5 concentration is established. A multiple linear regression model is proposed with the light radiance, meteorological elements (temperature, relative humidity, and wind speed) and terrain elements (elevation, slope, and terrain relief) as variables to estimate the ground PM2.5 concentration at 56 air quality monitoring stations in the Pearl River Delta (PRD) urban agglomeration from 2018 to 2019, and the accuracy of model estimation is tested. The results indicate that the R2 value between the model-estimated and measured values is 0.82 in the PRD region, and the model attains a high estimation accuracy. Moreover, the estimation accuracy of the model exhibits notable temporal and spatial heterogeneity. This study, to a certain extent, mitigates the shortcomings of traditional ground PM2.5 concentration monitoring methods with a high cost and low spatial resolution and complements satellite remote sensing technology. This study extends the use of LJ1-01 nighttime light remote sensing images to estimate nighttime PM2.5 concentrations. This yields a certain practical value and potential in nighttime ground PM2.5 concentration inversion.

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Zhang, Z., W. Feng, T. Wang, Y. Zhang, and L. Ding. "AN IMPROVED AERIAL REMOTE SENSING IMAGE DEFOGGING METHOD BASED ON DARK CHANNEL PRIOR INFORMATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 1025–30. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1025-2017.

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Aerial remote sensing image is widely used due to its high resolution, abundant information and convenient processing. However, its image quality is easily influenced by clouds and fog. In recent years, fog and haze air pollution is becoming more and more serious in the north of China and its influence on aerial remote sensing image quality is especially obvious. Considering the characters that aerial remote image is usually in huge amount of data and seldom covers sky area, this paper proposes an improved aerial remote sensing image defogging method based on dark channel prior information. First, a 2&thinsp;% linear stretching is applied to eliminate the haze offset effect and provide a better initial value for later defogging processing. Then the dark channel prior image is obtained by calculating the minimum values of r, g, b channels of each pixel directly. Subsequently, according to the particularity of aerial image, the adaptive threshold t0 is set up to improve the defogging effect. Finally, to improve the color cast phenomenon, a way called automatic color method is introduced to enhance the visual effect of defogged image. Experiments are performed on normal image in fog and on aerial remote sensing image in fog. Experimental results prove that the proposed method can obtain the defogged image with better visual effect and image quality. Moreover, the improved method significantly balances the color information in the defogged image and efficiently avoids the color cast phenomenon.

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Law, Katharine S., Andreas Stohl, Patricia K. Quinn, Charles A. Brock, John F. Burkhart, Jean-Daniel Paris, Gerard Ancellet, et al. "Arctic Air Pollution: New Insights from POLARCAT-IPY." Bulletin of the American Meteorological Society 95, no. 12 (December 1, 2014): 1873–95. http://dx.doi.org/10.1175/bams-d-13-00017.1.

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Given the rapid nature of climate change occurring in the Arctic and the difficulty climate models have in quantitatively reproducing observed changes such as sea ice loss, it is important to improve understanding of the processes leading to climate change in this region, including the role of short-lived climate pollutants such as aerosols and ozone. It has long been known that pollution produced from emissions at midlatitudes can be transported to the Arctic, resulting in a winter/spring aerosol maximum known as Arctic haze. However, many uncertainties remain about the composition and origin of Arctic pollution throughout the troposphere; for example, many climate–chemistry models fail to reproduce the strong seasonality of aerosol abundance observed at Arctic surface sites, the origin and deposition mechanisms of black carbon (soot) particles that darken the snow and ice surface in the Arctic is poorly understood, and chemical processes controlling the abundance of tropospheric ozone are not well quantified. The International Polar Year (IPY) Polar Study using Aircraft, Remote Sensing, Surface Measurements and Models, Climate, Chemistry, Aerosols and Transport (POLARCAT) core project had the goal to improve understanding about the origins of pollutants transported to the Arctic; to detail the chemical composition, optical properties, and climate forcing potential of Arctic aerosols; to evaluate the processes governing tropospheric ozone; and to quantify the role of boreal forest fires. This article provides a review of the many results now available based on analysis of data collected during the POLARCAT aircraft-, ship-, and ground-based field campaigns in spring and summer 2008. Major findings are highlighted and areas requiring further investigation are discussed.

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Journal articles on the topic 'Air – Pollution – Remote sensing'

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