scholarly journals The Characteristics of PM2.5 Pollution and Policy Implications in Chungcheong Region

2021 ◽  
Vol 43 (6) ◽  
pp. 407-418
Author(s):  
Jiyoung Gong ◽  
Changsub Shim ◽  
Ki-Chul Choi ◽  
Sungyong Gong
Keyword(s):  
Power Generation ◽  
Air Quality ◽  
Air Pollutants ◽  
Air Pollutant ◽  
Policy Implications ◽  
Pollutant Emission ◽  
Air Quality Modelling ◽  
Emission Volume ◽  
High Level ◽  
Pm2.5 Concentration

Objectives : This study aims to discuss air quality policy improvement that reflect regional characteristics through analyzing recent PM2.5 concentration, air pollutant emission sources and those contributions to annual PM2.5 concentration in Chungcheong region (Daejeon Metropolitan City, Sejong Metropolitan Autonomous City, the Province of Chungcheongnam-do, and Chungcheongbuk-do) in South Korea. In addition, we identified the characteristics of the PM2.5 pollution at the level of fundamental local government, and demonstrated the number of vulnerable population exposed to high level of PM2.5 concentration in order to propose policy implications in Chungcheong region.Methods : Based on the national emissions estimates (CAPSS: Clean Air Policy Support System) and air quality modelling system, major sectors/sources of air pollutants emission and national contributions of PM2.5 concentrations in Chungcheong region were analyzed. Furthermore, the study identified the number of people exposed to the higher PM2.5 concentrations (>25 µg/m3) by the measurement data and demographics available in 2019.Results and Discussion : The national air pollutants emissions in Chungcheong region were emitted from Chungnam (about 59% of NOx emission volume, 89% of SOx, 70% of NH3, 54% of VOCs, 79% of PM2.5, and 68% of TSP respectively), mainly from industry, domestic, energy, and road sector. According to the results of the air quality modelling, Chungcheong region also had the largest contribution on the average annual PM2.5 concentration in South Korea (27%). Chungnam emitted the largest emission volume of air pollutants, mainly from industry and power generation sectors (especially in Dangjin, Seosan, and Boryeong), while Asan, Yesan, Hongseong, and Cheongyang were classified as the areas with higher PM2.5 concentrations (>25 µg/m3), showing a gap between the areas with large emission volume and high concentration. Chungbuk and Sejong had higher annual PM2.5 concentration due to the influence of external sources and their geographical characteristics. The largest vulnerable population (over 65 years old and under 18 years old) exposed to high PM2.5 concentrations annually lived in Cheongju. Chungbuk had about 40% more air pollutant emission volume than Chungnam, but about 17% more vulnerable population.Conclusions : At the current stage of “master plan” in Chungcheong region, it is important to mitigate air pollutants emissions on the basis of the local emissions characteristic at the level of fundamental local government (such as industry sector in Dangjin, Seosan, and Danyang/ Domestic buring in Cheongju, Cheonan, and Daejeon/power generation in Boryeong, Taean and Dangjin/ road in Daejeon, Cheongju, and Cheoan). In addition, Chungbuk requires management of the areas with higher PM2.5 concentration such as Goesan, Boeun, Okcheon, and Yeongdong located outside “air control zone”. To reduce high level of PM2.5 concentration in Chungcheong region, cooperation with neighboring local governments such as Gyeonggi Province is crucial, and policy solutions are needed between the stakeholders to resolve the disparity issues between areas with larger emission volume and higher PM2.5 concentration.

scholarly journals Sectorial and regional uncertainty analysis of the contribution of anthropogenic emissions to regional and global PM<sub>2.5</sub> health impacts

2017 ◽  
Author(s):  
Monica Crippa ◽  
Greet Janssens-Maenhout ◽  
Diego Guizzardi ◽  
Rita Van Dingenen ◽  
Frank Dentener
Keyword(s):  
Power Generation ◽  
Air Quality ◽  
Human Health ◽  
Emission Inventory ◽  
Air Pollutant ◽  
Health Impacts ◽  
Pollutant Emission ◽  
Emission Inventories ◽  
Anthropogenic Emission ◽  
Relative Contribution

Abstract. In this work we couple the HTAPv2.2 global air pollutant emission inventory with the global source receptor model TM5-FASST to evaluate the relative contribution of the major anthropogenic emission sources (power generation, industry, ground transport, residential, agriculture and international shipping) to air quality and human health in 2010. We focus on particulate matter (PM) concentrations because of the relative importance of PM2.5 emissions in populated areas and the proven cumulative negative effects on human health. We estimate that in 2010 regional annual averaged anthropogenic PM2.5 concentrations varied between ca. 1 and 40 μg/m3 depending on the region, with the highest concentrations observed in China and India, and lower concentrations in Europe and North America. The relative contribution of anthropogenic emission source sectors to PM2.5 concentrations varies between the regions. European PM pollution is mainly influenced by the agricultural and residential sectors, while the major contributing sectors to PM pollution in Asia and the emerging economies are the power generation, industrial and residential sectors. We also evaluate the emission sectors and emission regions in which pollution reduction measures would lead to the largest improvement on the overall air quality. We show that in order to improve air quality, regional policies should be implemented (e.g. in Europe) due to the transboundary features of PM pollution. In addition, we investigate emission inventory uncertainties and their propagation to PM2.5 concentrations, in order to identify the most effective strategies to be implemented at sector and regional level to improve emission inventories knowledge and air quality. We show that the uncertainty of PM concentrations depends not only on the uncertainty of local emission inventories but also on that of the surrounding regions. Finally, we propagate emission inventories uncertainty to PM concentrations and health impacts.

scholarly journals On the Temporal Variability of Air Pollutants’ Emissions – Case Study of Residential PM10 Emission in Silesian Metropolis

2020 ◽  
Vol 3 (1) ◽  
pp. 21-29
Author(s):  
Damian Zasina ◽  
Jarosław Zawadzki
Keyword(s):  
Air Pollutants ◽  
Air Pollutant ◽  
Hot Water ◽  
Pollutant Emission ◽  
Air Quality Modelling ◽  
Air Emission ◽  
Temporal Disaggregation ◽  
Monthly Distribution ◽  
Heating Degree Days

AbstractThe paper summarizes previous studies associated with carrying out of the air pollutant emission inventories. There are presented three approaches for obtaining monthly distribution of PM10 air emission: using expert’s judgement, modelling of the heating demand, and temporal disaggregation using the heating degree days (HDD). However some differences due to not considering hot water demand, it can be effectively used for obtaining temporal, and spatiotemporal distributions of air pollutants’ air emissions necessary for air quality modelling.

scholarly journals Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: Impacts of COVID-19 pandemic lockdown

2020 ◽  
Author(s):  
Shibao Wang ◽  
Yun Ma ◽  
Zhongrui Wang ◽  
Lei Wang ◽  
Xuguang Chi ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Low Cost ◽  
Air Pollutant ◽  
Mitigation Strategies ◽  
Traffic Emissions ◽  
Urban Air ◽  
Industrial Emissions ◽  
Gis Technology ◽  
Mobile Monitoring

Abstract. The development of low-cost sensors and novel calibration algorithms provides new hints to complement conventional ground-based observation sites to evaluate the spatial and temporal distribution of pollutants on hyper-local scales (tens of meters). Here we use sensors deployed on a taxi fleet to explore the air quality in the road network of Nanjing over the course of a year (Oct. 2019–Sep. 2020). Based on GIS technology, we develop a grid analysis method to obtain 50 m resolution maps of major air pollutants (CO, NO2, and O3). Through hotspots identification analysis, we find three main sources of air pollutants including traffic, industrial emissions, and cooking fumes. We find that CO and NO2 concentrations show a pattern: highways > arterial roads > secondary roads > branch roads > residential streets, reflecting traffic volume. While the O3 concentrations in these five road types are in opposite order due to the titration effect of NOx. Combined the mobile measurements and the stationary station data, we diagnose that the contribution of traffic-related emissions to CO and NO2 are 42.6 % and 26.3 %, respectively. Compared to the pre-COVID period, the concentrations of CO and NO2 during COVID-lockdown period decreased for 44.9 % and 47.1 %, respectively, and the contribution of traffic-related emissions to them both decreased by more than 50 %. With the end of the COVID-lockdown period, traffic emissions and air pollutant concentrations rebounded substantially, indicating that traffic emissions have a crucial impact on the variation of air pollutants levels in urban regions. This research demonstrates the sense power of mobile monitoring for urban air pollution, which provides detailed information for source attribution, accurate traceability, and potential mitigation strategies at urban micro-scale.

Stand-alone low-cost sensor network in the inner city of Munich for modeling urban air pollutants

2021 ◽  
Author(s):  
Adrian Wenzel ◽  
Jia Chen ◽  
Florian Dietrich ◽  
Sebastian T. Thekkekara ◽  
Daniel Zollitsch ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Sensor Network ◽  
Air Pollutants ◽  
Low Cost ◽  
Air Pollutant ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Small Scale ◽  
Urban Air

&lt;p&gt;Modeling urban air pollutants is a challenging task not only due to the complicated, small-scale topography but also due to the complex chemical processes within the chemical regime of a city. Nitrogen oxides (NOx), particulate matter (PM) and other tracer gases, e.g. formaldehyde, hold information about which chemical regime is present in a city. As we are going to test and apply chemical models for urban pollution &amp;#8211; especially with respect to spatial and temporally variability &amp;#8211; measurement data with high spatial and temporal resolution are critical.&lt;/p&gt;&lt;p&gt;Since governmental monitoring stations of air pollutants such as PM, NOx, ozone (O&lt;sub&gt;3&lt;/sub&gt;) or carbon monoxide (CO) are large and costly, they are usually only sparsely distributed throughout a city. Hence, the official monitoring sites are not sufficient to investigate whether small-scale variability and its integrated effects are captured well by models. Smart networks consisting of small low-cost air pollutant sensors have the ability to provide the required grid density and are therefore the tool of choice when it comes to setting up or validating urban modeling frameworks. Such sensor networks have been established and run by several groups, achieving spatial and temporal high-resolution concentration maps [1, 2].&lt;/p&gt;&lt;p&gt;After having conducted a measurement campaign in 2016 to create a high-resolution NO&lt;sub&gt;2&lt;/sub&gt; concentration map for Munich [3], we are currently setting up a low-cost sensor network to measure NOx, PM, O&lt;sub&gt;3&lt;/sub&gt; and CO concentrations as well as meteorological parameters [4]. The sensors are stand-alone, so that they do not demand mains supply, which gives us a high flexibility in their deployment. Validating air quality models not only requires dense but also high-accuracy measurements. Therefore, we will calibrate our sensor nodes on a weekly basis using a mobile reference instrument and apply the gathered sensor data to a Machine Learning model of the sensor nodes. This will help minimize the often occurring drawbacks of low-cost sensors such as sensor drift, environmental influences and sensor cross sensitivities.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;[1] Bigi, A., Mueller, M., Grange, S. K., Ghermandi, G., and Hueglin, C.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717&amp;#8211;3735, https://doi.org/10.5194/amt-11-3717-2018, 2018&lt;/p&gt;&lt;p&gt;[2] Kim, J., Shusterman, A. A., Lieschke, K. J., Newman, C., and Cohen, R. C.: The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors, Atmos. Meas. Tech., 11, 1937&amp;#8211;1946, https://doi.org/10.5194/amt-11-1937-2018, 2018&lt;/p&gt;&lt;p&gt;[3] Zhu, Y., Chen, J., Bi, X., Kuhlmann, G., Chan, K. L., Dietrich, F., Brunner, D., Ye, S., and Wenig, M.: Spatial and temporal representativeness of point measurements for nitrogen dioxide pollution levels in cities, Atmos. Chem. Phys., 20, 13241&amp;#8211;13251, https://doi.org/10.5194/acp-20-13241-2020, 2020&lt;/p&gt;&lt;p&gt;[4] Zollitsch, D., Chen, J., Dietrich, F., Voggenreiter, B., Setili, L., and Wenig, M.: Low-Cost Air Quality Sensor Network in Munich, EGU General Assembly 2020, Online, 4&amp;#8211;8 May 2020, EGU2020-19276, https://doi.org/10.5194/egusphere-egu2020-19276, 2020&lt;/p&gt;

FRI0531 AIR POLLUTANTS AND DEVELOPMENT OF INTERSTITIAL LUNG DISEASE IN PATIENTS WITH AUTOIMMUNE DISEASES

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 865.1-865
Author(s):  
H. H. Chen ◽  
W. C. Chao ◽  
Y. H. Chen ◽  
D. Y. Chen ◽  
C. H. Lin
Keyword(s):  
Air Quality ◽  
Autoimmune Diseases ◽  
Air Pollutants ◽  
Air Pollutant ◽  
Air Quality Monitoring ◽  
Newly Diagnosed ◽  
Science Technology ◽  
Air Quality Monitoring Stations ◽  
The Impact ◽  
Modest Level

Background:Interstitial lung disease (ILD) is characterized by progressive inflammation and fibrosis, and accumulating evidence have shown that exposure to air pollutants was associated with the development of ILD. Autoimmune diseases are highly correlated with ILD, including connective tissue disease-associated ILD (CTD-ILD) as well as interstitial pneumonia with autoimmune features (IPAF), and the development of ILD is a crucial cause of morbidity and mortality in patients with autoimmune diseases. One recent Taiwanese study reported that exposure to air pollutants was associated with incident systemic lupus erythematosus (SLE). However, the impact of air pollutants on the development of ILD among patients with autoimmune diseases remains unknown.Objectives:The study aimed to address the impact of accumulating exposure to air pollutant above moderate level, defined by Air Quality Index (AQI) value higher than 50, on the development of ILD in patients with autoimmune diseases including SLE, rheumatoid arthritis (RA) and primary Sjögren’s syndrome (SS).Methods:We used a National Health Insurance Research Database in Taiwan to enroll patients with SLE (International Classification of Diseases (ICD)-9 code 710.0, n=13,211), RA (ICD-9 code 714.0 and 714.30–714.33, n=32,373), and primary SS (ICD-9 code, 710.0, n=15,246) between 2001 and 2013. We identified newly diagnosed ILD cases (ICD-code 515) between 2012 and 2013 and selected age, sex, disease duration and index-year matched (1:4) patients as non-ILD controls. The hourly levels of air pollutants one year prior to the index-date were obtained from 60 air quality monitoring stations across Taiwan, and the air pollutants in the present study consisted of particulate matter <2.5 μm in size (PM2.5), particulate matter <10 μm in size (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2) and ozone (O3). We used a spatio-temporal model built by a deep-learning mechanism to estimate levels of air pollutants at 374 residential locations based on data of 3 air quality monitoring stations near the location (8). Notably, we used cumulative exposed hours to air pollutants higher than modest level, defined by AQI criteria, given that daily mean level of air pollutants might possibly underestimate the triggered inflammatory effect by a temporary exposure of high-level air pollutant. A conditional logistic regression was used to determine the association between exposure to air pollutant and the development of ILD, adjusting age, gender, Charlson Comorbidity Index (CCI), urbanization, family income, and medications for autoimmune diseases.Results:A total of 272 patients with newly diagnosed ILD were identified among patients with autoimmune diseases, including 39 with SLE, 135 with RA, and 98 with primary SS. We found that the duration of exposure to PM 2.5 higher than modest level was associated with the risk of ILD development in patients with SS (adjOR 1.07, 95% CI 1.01–1.13), and similar trends were also found in patients with SLE (adjOR 1.03, 95% CI 0.95–1.12) and RA (adjOR 1.03, 95% CI 0.99–1.07). Intriguingly, we observed an inverse correlation between the duration of exposure to O3 and the development of ILD in patients with SS (adjOR 0.83, 95% CI 0.70–0.99); however, the finding was not found in patients with SLE (adjOR 1.13, 95% CI 0.92–1.37) and RA (adjOR 0.98, 95% CI 0.87–1.11).Conclusion:In conclusion, we identified that longer exposure to PM2.5 higher than modest level tended to be associated with the development of ILD in patients with autoimmune diseases, mainly SS.References:[1] Araki T, Putman RK, Hatabu H, Gao W, Dupuis J, Latourelle JC, et al. Development and Progression of Interstitial Lung Abnormalities in the Framingham Heart Study. Am J Respir Crit Care Med 2016;194:1514-1522.[2] Tang KT, Tsuang BJ, Ku KC, Chen YH, Lin CH, Chen DY. Relationship between exposure to air pollutants and development of systemic autoimmune rheumatic diseases: a nationwide population-based case-control study. Ann Rheum Dis 2019;78:1288-1291.Disclosure of Interests:Hsin-Hua Chen: None declared, Wen-Cheng Chao: None declared, Yi-Hsing Chen Grant/research support from: Taiwan Ministry of Science and Technology, Taiwan Department of Health, Taichung Veterans General Hospital, National Yang-Ming University, GSK, Pfizer, BMS., Consultant of: Pfizer, Novartis, Abbvie, Johnson & Johnson, BMS, Roche, Lilly, GSK, Astra& Zeneca, Sanofi, MSD, Guigai, Astellas, Inova Diagnostics, UCB, Agnitio Science Technology, United Biopharma, Thermo Fisher, Gilead., Paid instructor for: Pfizer, Novartis, Johnson & Johnson, Roche, Lilly, Astra& Zeneca, Sanofi, Astellas, Agnitio Science Technology, United Biopharma., Speakers bureau: Pfizer, Novartis, Abbvie, Johnson & Johnson, BMS, Roche, Lilly, GSK, Astra& Zeneca, Sanofi, MSD, Guigai, Astellas, Inova Diagnostics, UCB, Agnitio Science Technology, United Biopharma, Thermo Fisher, Gilead., Der-Yuan Chen: None declared, Ching-Heng Lin: None declared

scholarly journals Analysis of Environmental Benefits of Shore Power for Preventing and Controlling Air Pollution Caused by Vessels at Berth

2018 ◽  
Vol 53 ◽  
pp. 04036 ◽  
Author(s):  
Cheng Jieling ◽  
Li Haibo
Keyword(s):  
Energy Conservation ◽  
Fuel Consumption ◽  
Air Pollutants ◽  
Emission Reduction ◽  
Electric Energy ◽  
Air Pollutant ◽  
Environmental Benefits ◽  
Pollutant Emission ◽  
Current Status ◽  
Fuel Consumption Rate

When vessels are berthed at ports, the air pollutants emitted by auxiliary engines will cause severe pollution to the ports and surrounding environments. In view of this situation, the author first summarizes the Chinese policies and policies of foreign countries on emission of air pollutants from vessels at berth, and then analyses the current status of and measures for control of air pollutant emission from vessels at berth. Secondly, the author analyses the environmental benefits of using shore power for better controlling air pollutant emission from vessels at berth, compares vessels using shore power with vessels using generated power in the energy conservation and emission reduction effects based on the fuel consumption rate of different auxiliary engines and current status of pollutant emission from power generation in China etc., analyses the current status of shore power application in China, estimates the energy conserved and emission reduced when shore power is used by vessels at berth. Thirdly, the author identifies the scale of electric energy replacement by, and environmental benefits of, shore power at ports in China. This paper delivers innovative approaches to the comparison between the effects of energy conservation and emission reduction based on fuel consumption rates of different auxiliary engines and estimation of conserved energy and reduced emission.

scholarly journals Long-Term Assessment of Air Quality and Identification of Aerosol Sources at Setúbal, Portugal

2020 ◽  
Vol 17 (15) ◽  
pp. 5447 ◽  
Author(s):  
Alexandra Viana Silva ◽  
Cristina M. Oliveira ◽  
Nuno Canha ◽  
Ana Isabel Miranda ◽  
Susana Marta Almeida
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Air Pollutants ◽  
Air Pollutant ◽  
Urban Traffic ◽  
Anthropogenic Sources ◽  
World Health ◽  
Mitigation Measures ◽  
Guideline Values ◽  
Health Organization

Understanding air pollution in urban areas is crucial to identify mitigation actions that may improve air quality and, consequently, minimize human exposure to air pollutants and their impact. This study aimed to assess the temporal evolution of the air quality in the city of Setúbal (Portugal) during a time period of 10 years (2003–2012), by evaluating seasonal trends of air pollutants (PM10, PM2.5, O3, NO, NO2 and NOx) measured in nine monitoring stations. In order to identify emission sources of particulate matter, PM2.5 and PM2.5–10 were characterized in two different areas (urban traffic and industrial) in winter and summer and, afterwards, source apportionment was performed by means of Positive Matrix Factorization. Overall, the air quality has been improving over the years with a decreasing trend of air pollutant concentration, with the exception of O3. Despite this improvement, levels of PM10, O3 and nitrogen oxides still do not fully comply with the requirements of European legislation, as well as with the guideline values of the World Health Organization (WHO). The main anthropogenic sources contributing to local PM levels were traffic, industry and wood burning, which should be addressed by specific mitigation measures in order to minimize their impact on the local air quality.

scholarly journals Enhanced atmospheric oxidizing capacity in simulating air quality with updated emission inventories for power plants especially for haze periods over East China

2017 ◽  
Author(s):  
Lei Zhang ◽  
Tianliang Zhao ◽  
Sunling Gong ◽  
Shaofei Kong ◽  
Lili Tang ◽  
...  
Keyword(s):  
Air Quality ◽  
Power Plants ◽  
Emission Inventory ◽  
Environmental Changes ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Atmospheric Environment ◽  
Pollutant Emission ◽  
East China ◽  
Emission Inventories

Abstract. Air pollutant emissions play a determinant role in deteriorating air quality. However, an uncertainty in emission inventories is still the key problem for modeling air pollution. In this study, an updated emission inventory of coal-fired power plants (UEIPP) based on online monitoring data in Jiangsu province of East China for the year of 2012 was implemented in the widely used Multi-resolution Emission Inventory for China (MEIC). By employing the Weather Research and Forecasting Model with Chemistry (WRF-Chem), two simulations were executed to assess the atmospheric environmental change by using the original MEIC emission inventory and the MEIC inventory with the UEIPP. A synthetic analysis shows that (1) compared to the power emissions of MEIC, PM2.5, PM10, SO2 and NOx were lower, and CO, black carbon (BC), organic carbon (OC) and NMVOCs were higher in the UEIPP, reflecting a large discrepancy in the power emissions over East China; (2) In accordance with the changes of UEIPP, the modeled concentrations were reduced for SO2 and NO2, and increased for most areas of primary OC, BC and CO, whose concentrations in atmosphere are highly dependent on emission changes. (3) Interestingly, when the UEIPP was used, the atmospheric oxidizing capacity significantly reinforced, reflecting by increased oxidizing agents, e.g. O3 and OH, thus directly strengthened the chemical production from SO2 and NOx to sulfate and nitrate, which offset the reduction of primary PM2.5 emissions especially in the haze days. This study indicated the importance of updating air pollutant emission inventories in simulating the complex atmospheric environment changes with the implications on air quality and environmental changes.

scholarly journals Urban PM2.5 Concentration Prediction via Attention-Based CNN–LSTM

2020 ◽  
Vol 10 (6) ◽  
pp. 1953 ◽  
Author(s):  
Songzhou Li ◽  
Gang Xie ◽  
Jinchang Ren ◽  
Lei Guo ◽  
Yunyun Yang ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Air Pollutant ◽  
Quality Data ◽  
Coefficient Of Determination ◽  
Series Data ◽  
The Past ◽  
Lstm Network ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN–LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance.

scholarly journals Ozone in Spain's National Parks and Protected Forests

2007 ◽  
Vol 7 ◽  
pp. 67-77 ◽  
Author(s):  
María J. Sanz ◽  
Francisco Sanz ◽  
Vicent Calatayud ◽  
Gerardo Sanchez-Peña
Keyword(s):  
Air Quality ◽  
Protected Areas ◽  
National Parks ◽  
Canary Islands ◽  
Air Pollutants ◽  
Air Pollutant ◽  
Passive Samplers ◽  
Remote Areas ◽  
Parks And Protected Areas ◽  
Ozone Levels

In general, it is difficult to measure air pollutant concentrations in remote areas, as they are mostly national parks and protected areas. Passive samplers provide an accurate and inexpensive method for measuring cumulative exposures of different air pollutants. They have been used to collect ozone data in both laboratory and field at different geographical scales. The objective of the present study is to fill the knowledge gap regarding air quality in remote areas of Spain, such as national parks and protected areas. Because there were no systematic data sets on the main air pollutants that could affect these areas, an air quality measurement network was established between 2001 and 2004 on 19 locations inside Spanish national parks and protected areas. The data collected suggest that ozone levels in mountainous areas are high enough to affect sensitive vegetation. Most of the locations registered moderate-to-high ozone levels, with important interannual variability. Altitudinal ozone gradients were observed in most of the parks with complex topography due to the establishment of local circulations that incorporate polluted air masses from polluted airsheds or even long-range transport (i.e., Canary Islands). Different latitude-dependent, yearly cycles were also observed, showing two, one, or no clear peaks depending on the region. These findings extend to the most southerly locations, except in the Canary Islands, where pollution transported from other regions in the upper transport layers probably led to the high concentrations observed.

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