Traffic Stations Research Articles

Detecting Moving Trucks on Roads Using Sentinel-2 Data

In most countries, freight is predominantly transported by road cargo trucks. We present a new satellite remote sensing method for detecting moving trucks on roads using Sentinel-2 data. The method exploits a temporal sensing offset of the Sentinel-2 multispectral instrument, causing spatially and spectrally distorted signatures of moving objects. A random forest classifier was trained (overall accuracy: 84%) on visual-near-infrared-spectra of 2500 globally labelled targets. Based on the classification, the target objects were extracted using a developed recursive neighbourhood search. The speed and the heading of the objects were approximated. Detections were validated by employing 350 globally labelled target boxes (mean F1 score: 0.74). The lowest F1 score was achieved in Kenya (0.36), the highest in Poland (0.88). Furthermore, validated at 26 traffic count stations in Germany on in sum 390 dates, the truck detections correlate spatio-temporally with station figures (Pearson r-value: 0.82, RMSE: 43.7). Absolute counts were underestimated on 81% of the dates. The detection performance may differ by season and road condition. Hence, the method is only suitable for approximating the relative truck traffic abundance rather than providing accurate absolute counts. However, existing road cargo monitoring methods that rely on traffic count stations or very high resolution remote sensing data have limited global availability. The proposed moving truck detection method could fill this gap, particularly where other information on road cargo traffic are sparse by employing globally and freely available Sentinel-2 data. It is inferior to the accuracy and the temporal detail of station counts, but superior in terms of spatial coverage.

Air pollution and health impacts during the COVID-19 lockdowns in Grenoble, France

It is undeniable that exposure to outdoor air pollution impacts the health of populations and therefore constitutes a public health problem. Any actions or events causing variations in air quality have repercussions on populations’ health. Faced with the worldwide COVID-19 health crisis that began at the end of 2019, the governments of several countries were forced, in the beginning of 2020, to put in place very strict containment measures that could have led to changes in air quality. While many works in the literature have studied the issue of changes in the levels of air pollutants during the confinements in different countries, very few have focused on the impact of these changes on health risks. In this work, we compare the 2020 period, which includes two lockdowns (March 16 - May 10 and a partial shutdown Oct. 30 - Dec. 15) to a reference period 2015–2019 to determine how these government-mandated lockdowns affected concentrations of NO2, O3, PM2.5, and PM10, and how that affected human health factors, including low birth weight, lung cancer, mortality, asthma, non-accidental mortality, respiratory, and cardiovascular illnesses. To this end, we structured 2020 into four periods, alternating phases of freedom and lockdowns characterized by a stringency index. For each period, we calculated (1) the differences in pollutant levels between 2020 and a reference period (2015–2019) at both background and traffic stations; and (2) the resulting variations in the epidemiological based relative risks of health outcomes. As a result, we found that relative changes in pollutant levels during the 2020 restriction period were as follows: NO2 (−32%), PM2.5 (−22%), PM10 (−15%), and O3 (+10.6%). The pollutants associated with the highest health risk reductions in 2020 were PM2.5 and NO2, while PM10 and O3 changes had almost no effect on health outcomes. Reductions in short-term risks were related to reductions in PM2.5 (−3.2% in child emergency room visits for asthma during the second lockdown) and NO2 (−1.5% in hospitalizations for respiratory causes). Long-term risk reductions related to PM2.5 were low birth weight (−8%), mortality (−3.3%), and lung cancer (−2%), and to NO2 for mortality (−0.96%). Overall, our findings indicate that the confinement period in 2020 resulted in a substantial improvement in air quality in the Grenoble area.

Ecological carrying capacity and sustainability assessment for coastal zones: A novel framework based on spatial scene and three-dimensional ecological footprint model

The ecological carrying capacity (ECC) assessment in coastal zones is essential for sustainable coastal management, but there remains a lack of a more effective assessment method to be applied across broad contexts. In this study, we proposed the concept of spatial scene, a geographical unit with a coordinate position, and high unification in social-economic attributes, land cover, ecological function, and externalities, to substitute for the land use/land cover (LULC) in the traditional three-dimensional ecological footprint (EF3D) model, thereby establishing a novel framework for coastal ECC (CECC) assessment. The coastal zone of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was chosen to examine the applicability and reliability of our framework. Results showed that the CECC estimated by spatial scene in the study area reached 0.1877 gha per capita, totaled 3.99 million gha in 2019, and the scenes of marine capture and forest provided the largest CECC. The per capita ecological footprint size (EFsize), ecological footprint depth (EFdepth), and EF3D reached 0.1684 gha, 14.35, and 2.42 gha, respectively, representing unsustainable development in the GBA coastal zone. The EF3D mainly distributed in scenes of grassland, forest, industrial, marine capture, coastal intertidal and offshore (CIO) port-shipping, traffic station, dryland, and CIO industrial-urban, while only the scenes of services and CIO tourism-entertainment were within CECC and therefore sustainable. Hong Kong, Huizhou, and Dongguan had the largest per capita EF3D. Compared to our results, the CECC and EFsize estimated by the traditional EF3D model were respectively 18% and 6% lower, while their EFdepth and EF3D were respectively 21% and 13% higher, which should be attributed to the significant differences in classification standard and scale between spatial scene and LULC. Our results showed higher correlations and more significant relationships with total gross domestic product (GDP), marine GDP, and main energy EF than those based on the traditional LULC, indicating a better reflection of the economic development status, energy consumption structure, and marine economic development modes by our framework. It is recommended to accelerate the industrial transformation and upgrading, and strengthen the conservation of ecological, agricultural, and marine space, in order to promote the sustainable development of GBA coastal zone. Our study revealed that our framework is capable of serving as a more effective and accurate method for assessing CECC and sustainability.

Urban Vitality and its Influencing Factors: Comparative Analysis Based on Taxi Trajectory Data

Taxis have always been an important part of the urban transportation system in metropolises since they can meet many demands and greatly improve travel efficiency. This study explores the distributions of hot spot destinations of taxi, land surface temperature data, and VIIRS nighttime light data in Beijing, Shanghai, and Wuhan. This study further selects point of interests (POIs) that are very close to taxi ridership patterns. This paper finally uses a geographically weighted regression (GWR) model to examine the relationship between population flow by taxi and urban vitality, which is represented by land surface temperature, nighttime light data and influential POIs. The results suggest that urban structure has an impact on the distribution of drop-off points and nighttime light. The implementation of the relevant policies is revealed to have a certain influence on the alleviation of the urban thermal environment. Traffic stations and financial facilities contribute to an increase in travel, and their influences vary spatially. Similarly, the relationships between the drop-off points and LST and NTL vary spatially. This research aids in understanding population flow and urban vitality development. In addition, this study provides valuable results for policy-makers to promote urban vitality development.

Okresowe zmiany prędkości poruszania się pojazdów po polskich drogach na przykładzie wybranych odcinków autostrad = Periodic changes in road-traffic speed as exemplified along selected sections of Polish motorway

The speeds at which cars cover individual sections of a route depend on many factors, of which the most important relate to technical conditions in general or on the road surface, speed limits, volumes of traffic and weather conditions. This publication concerns a selected range of issues related to road traffic and is based on research conducted to show periodic changes in vehicle speeds along Polish roads. It represents a step towards increased accuracy of determination of road-traffic conditions, encouraging better prediction and discernment of patterns, as well as greater insight into influential factors. Knowledge of periodic changes in vehicle speeds gains further use in road-traffic modelling, for example at National Traffic Management Centres. Calculations of the speeds of vehicles covering test-sections of Polish Motorways involved twelve kilometre-long segments of the A1, A4 and A8. Vehicle probe data collected over the four years 2014‑2018 represented the source used in calculating speeds. From within the set of data, further consideration was confined to periods not impacted significantly by such other factors as public holidays, weekends, adverse weather, darkness or maintenance works. The speeds at which vehicles covered the selected sections were calculated, aggregated separately for the light and heavy categories, and then analysed for their daytime and annual variability. The first conclusion to be drawn from analysis of the results concerns the inevitable impact on speed of the “traffic volume” factor. However, “traffic vehicular structure” is another factor important in analyses of traffic fluctuations. Were this research to be developed further that would entail further-reaching inference in regard to the nature of traffic and types of trip being made. The research described here can thus be considered a first step in the planning of other, more-accurate measurement campaigns that will require greater preparation in advance, and well as resort to more-specialised equipment. The present homogeneous study nevertheless offers grounds for inferences as to both the nature of the traffic it describes, and the types of trips being made. A continuation of research would thus extend analyses around the clock (covering 24-hour days rather than just “daytime”), as well as to other days of the week, such as Saturdays and Sundays, to holidays, and even to separate days of the week within the overall set of working days. The number of test-sections might also be increased, and enriched by new types, classes, geometries and locations of routes allowing for fuller inference. In particular, additional locations with Continuous Traffic Measurement Stations would provide for the fuller correlation of vehicle speeds with volumes of traffic. Similarly, filtered datasets factoring in adverse weather conditions, Sun low above the horizon and periods of road maintenance could all provide for more research of a more comprehensive nature.

Pollutant Concentration Changes During the COVID-19 Lockdown in Barcelona and Surrounding Regions: Modification of Diurnal Cycles and Limited Role of Meteorological Conditions

One of the consequences of the COVID-19 lockdowns has been the modification of the air quality in many cities around the world. This study focuses on the variations in pollutant concentrations and how important meteorological conditions were for those variations in Barcelona and the surrounding area during the 2020 lockdown. Boundary-layer height, wind speed, and precipitation were compared between mid-March and April 2016–2019 (pre-lockdown) and the same period in 2020 (during lockdown). The results show the limited influence of meteorological factors on horizontal and vertical dispersion conditions. Compared with the pre-lockdown period, during lockdown the boundary-layer height slightly increased by between 5% and 9%, mean wind speed was very similar, and the fraction of days with rainfall increased only marginally, from 0.33 to 0.34, even though April 2020 was extremely wet in the study area. Variations in nitrogen dioxide (hbox {NO}_{{2}}), particulate matter with a diameter less than 10 {mu }m (PM10), and ozone (hbox {O}_{{3}}) concentrations over a 10-year period showed a 66% reduction in hbox {NO}_{{2}}, 37% reduction in PM10, and 27% increase in hbox {O}_{{3}} at a traffic station in Barcelona. The differences in the daily concentration cycle between weekends and weekdays were heavily smoothed for all pollutants considered. The afternoon hbox {NO}_{{2}} peak at the traffic station was suppressed compared with the average daily cycle. The analysis of ozone was extended to the regional scale, revealing lower concentrations at rural sites and higher ones in urban zones, especially in Barcelona and the surrounding area. The results presented not only complement previous air quality COVID-19 lockdown studies but also provide insights into the effects of road-traffic reduction.

Highway Regional Classification Method Based on Traffic Flow Characteristics for Highway Safety Assessment.

Accurate regional classification of highways is a critical prerequisite to implement a tailored safety assessment. However, there has been inadequate research on objective classification considering traffic flow characteristics for highway safety assessment purposes. We propose an objective and easily applicable classification method that considers the administrative divisions of South Korea. We evaluated the feasibility of this method through various theoretical analysis techniques using the data collected from 536 permanent traffic volume counting stations for the national highways in South Korea in 2019. The ratio of the annual average hourly traffic volume to the annual average daily traffic was used as the explanatory variable. The corresponding results of factor and cluster analyses with this ratio showed a 61% concordance with the urban, suburban, and rural areas classified by the administrative divisions. The results of two-sample goodness-of-fit tests also confirmed that the difference in the three distributions of hourly volume ratios was statistically significant. The results of this study can help enhance highway safety and facilitate the development and application of more appropriate highway safety assessment tools, such as Road Assessment Programs or crash prediction models, for specific regions using the proposed method.

Has COVID-19 Lockdown Affected on Air Quality?—Different Time Scale Case Study in Wrocław, Poland

Due to the COVID-19 pandemic, there are series of negative economic consequences, however, in limiting mobility and reducing the number of vehicles, positive effects can also be observed, i.e., improvement of air quality. The paper presents an analysis of air quality measured by concentrations of NO2, NOx and PM2.5 during the most restrictive lockdown from 10 March to 31 May 2020 on the case of Wrocław. The results were compared with the reference period—2016–2019. A significant reduction in traffic volume was identified, on average by 26.3%. The greatest reduction in the concentration of NO2 and NOx was recorded at the station farthest from the city center, characterized by the lowest concentrations: 20.1% and 22.4%. Lower reduction in the average concentrations of NO2 and NOx was recorded at the municipal station (7.9% and 7.7%) and the communication station (6.7% and 10.2%). Concentrations of PMs in 2020 were on average 15% and 13.4% lower than in the reference period for the traffic station and the background station. The long-term impact of the lockdown on air quality was also examined. The analysis of the concentrations of the pollutants throughout 2020, and in the analyzed period of 2021, indicated that the reduction of concentrations and the improvement in air quality caused by the restrictions should be considered as a temporary anomaly, without affecting long-term changes and trends.

Traffic station classification based on deep spatio-temporal network

Employing existing traffic data for the accurate classification of roads can provide significant references for the planning and construction of urban traffic infrastructure. This paper devises a novel deep learning framework for traffic station classification (DeepTSC) which classifies the traffic monitoring stations to in turn classify the road where these stations are located. The monitoring data of these traffic stations are interdependent with regards to both their spatial and temporal dimensions. We design a one-dimensional convolution layer for preliminary feature extraction and feature fusion. Following this, we employ a hybrid combination of the multilayer dilated convolution, long short-term memory network (LSTM) and attention mechanism, to extract the spatio-temporal features of the traffic data. Experiments on a public California freeway dataset show that the DeepTSC model's classification results outperform other existing state-of-the-art methods, with its accuracy being at least 1.4% higher than other models.

ТЕНДЕНЦИИ РАЗВИТИЯ АРХИТЕКТУРЫ МОСКОВСКОГО МЕТРОПОЛИТЕНА В ПЕРИОД С 1935 ПО 2000ГГ.

For 200 years, the Metro has been carrying out most of the passenger traffic in large cities and metropolitan areas. The metro architecture embodies cultural ideals, historical milestones and the achievements of society in various fields. The metro is the transport frame of the city, participates in the formation of the urban ensemble. Sustainable development of the metro requires a comprehensive study of its architecture, analysis of domestic and foreign analogues: the prerequisites and chronology of metro development, identification of concepts, directions, principles and techniques of the architectural formation of metro objects. A systematic approach to architectural analysis and design of the metro, identifying the importance of the role of an architect allows to determine the prospects and vectors for the development of transport infrastructure, improve the quality and safety of passenger traffic, design energy-efficient, autonomous, economical, aesthetic and ergonomic metro stations. The high rates of modern metro design require the development of measures to preserve the unity of the metro lines and communication with the city space. Systematization and classification of trends in the development of metro architecture in the period from 1823 to 2000 will allow to determine the arsenal of architectural techniques, to develop urban planning approaches to the architectural solution of the stations

The positive impact of the first-wave COVID-19 lockdown on urban air quality in Greece

BACKGROUND AND AIM: Following the first confirmed cases of COVID-19 during the first wave of the pandemic, the Government of Greece imposed a nationwide lockdown on March 23rd 2019, one of the first in Europe. Our study assesses the impact of this lockdown on the air quality of Greece’s two largest cities, Athens and Thessaloniki. METHODS: The average monthly concentrations of particulate matter (PM10 and PM2.5) and nitrogen oxides (NO and NO2) measured on a 24-hour basis were obtained from the Greek Ministry of the Environment and Energy. In order to assess the impact of the lockdown on air quality, data were divided into three periods: (a) pre-lockdown (1 September 2019 – 22 March 2020), (b) during lockdown (23 March 2020 – 3 May 2020), and (c) post-lockdown (4 May 2020 – 6 November 2020), which overlapped with the beginning of the second wave COVID-19. RESULTS:Nitrogen oxide showed the most striking decrease in Athens, ranging from 6 to 84% (during lockdown) and from 12 to 66% (post lockdown), respectively, and was most prominent at the urban traffic stations. Compared to the pre-lockdown period, Athens also experienced significant reductions in PM10 (13–38%), PM2.5 (1-40%) and NO2 (9–65%) concentrations. In Thessaloniki the most marked decrease was observed at the urban traffic stations for PM10 and PM2.5, where a 39% and 31% decrease was respectively observed when comparing the lockdown and post-lockdown periods. CONCLUSIONS:Our results indicate that there is a relationship between air pollution and lockdown measures. The time series of the 24-hour data for PM10, PM2.5, NO and NO2 concentrations showed a decrease during the six weeks of lockdown in both cities. The observed changes could be attributed to lower traffic volumes, working remotely and temporary suspension of industrial activities. KEYWORDS: air pollution, particulate matter, oxides of nitrogen

A Study of Traffic Emissions Based on Floating Car Data for Urban Scale Air Quality Applications

Urban air quality in cities is strongly influenced by road traffic emissions. Micro-scale models have often been used to evaluate the pollutant concentrations at the scale of the order of meters for estimating citizen exposure. Nonetheless, retrieving emissions information with the required spatial and temporal details is still not an easy task. In this work, we use our modelling system PMSS (Parallel Micro Swift Spray) with an emission dataset based on Floating Car Data (FCD), containing hourly data for a large number of road links within a 1 × 1 km2 domain in the city of Rome for the month of May 2013. The procedures to obtain both the emission database and the PMSS simulations are hosted on CRESCO (Computational Centre for Research on Complex Systems)/ENEAGRID HPC facilities managed by ENEA. The possibility of using such detailed emissions, coupled with HPC performance, represents a desirable goal for microscale modeling that can allow such modeling systems to be employed in quasi-real time and nowcasting applications. We compute NOx concentrations obtained by: (i) emissions coming from prescribed hourly modulations of three types of roads, based on vehicle flux data in the FCD dataset, and (ii) emissions from the FCD dataset integrated into our modelling chain. The results of the simulations are then compared to concentrations measured at an urban traffic station.

Urban population exposure forecast system to predict NO2 impact by a building-resolving multi-scale model approach

Operational forecasting systems based on chemical transport models (CTMs) nowadays generally produce concentration maps with a resolution in the order of 2–5 km, very rarely exceeding the sub-kilometre scale. The main reason for this restriction is the prohibitive computing cost that a simulation covering an entire country would have if set-up with a resolution in the order of meters. In this paper a hybrid forecast system, relying on the WRF-Chem model coupled with the PMSS Lagrangian modelling suite, has been developed and applied for each day of February 2019, to predict hourly NO2 and NOx concentrations with a spatial resolution of 4 m, for the urban area of Modena (a city located in the central Po Valley). Simulated meteorological fields (temperature, wind speed and direction) were assessed at three urban stations, compliant with WMO standards, and modelled concentrations were compared with measurements at two urban air quality stations located at background and traffic sites.Results show that meteorological variables are well captured by the hybrid system and statistical performances are in line with the benchmark values suggested by the European Environmental Agency and with similar case studies focusing on the same area. Modelled NO2 and NOx concentrations, notwithstanding a slight underestimation mainly evident at urban traffic stations for NOx, present a large agreement with related observations. The NO2 Model Quality Objective, as defined by Fairmode guidelines, was met for both the urban stations and the other statistical indexes considered in the evaluation fulfilled the acceptance criteria for dispersion modelling in urban environment, for both NO2 and NOx concentrations.In the second section of the study, the population exposure to forecasted NO2 concentrations has been evaluated adopting a generic model of dynamic population activity. The population was distributed at hourly time steps in specific urban micro-environments at the same resolution of the concentration maps (4 m) and the short-term exposure has been computed as the product between the population density in each model cell and related surface NO2 concentrations. An infiltration factor was also applied to estimate indoor concentrations. The hybrid system was shown to be particularly suited for assessing short-term peak exposure in areas influenced by traffic emissions. On the other hand, due to the limited time spent by the population within traffic related environments, the long-term population exposure calculated by the hybrid system tends to be similar to the WRF-Chem stand-alone estimate.

Source Apportionment and Toxicity of PM in Urban, Sub-Urban, and Rural Air Quality Network Stations in Catalonia

Air quality indicators, i.e., PM10, NO2, O3, benzo[a]pyrene, and several organic tracer compounds were evaluated in an urban traffic station, a sub-urban background station, and a rural background station of the air quality network in Catalonia (Spain) from summer to winter 2019. The main sources that contribute to the organic aerosol and PM toxicity were determined. Traffic-related air pollution dominated the air quality in the urban traffic station, while biomass burning in winter and secondary organic aerosol (SOA) in summer impact the air quality in the sub-urban and rural background stations. Health risk assessment for chronic exposure over the past decade, using WHO air quality standards, showed that NO2, PM10 and benzo[a]pyrene from traffic emissions pose an unacceptable risk to the human population in the urban traffic station. PM10 and benzo[a]pyrene from biomass burning were unacceptably high in the sub-urban and rural background stations. Toxicity tests of the PM extracts with epithelial lung cells showed higher toxicity in wintertime samples in the sub-urban and rural stations, compared to the urban traffic station. These results require different mitigation strategies for urban and rural sites in order to improve the air quality. In urban areas, traffic emissions are still dominating the air quality, despite improvements in the last years, and may directly be responsible for part of the SOA and O3 levels in sub-urban and rural areas. In these later areas, air pollution from local biomass burning emissions are dominating the air quality, essentially in the colder period of the year.

Investigation of Saharan dust influence on PM10 concentration using two methods in Gabès, Tunisia

Our main concern through this research is the extent of the contribution of desert dust on PM10 concentration, depending on two methods in the Tunisian region called Gabes during a desert dust period. To make sure of the desert dust phenomenon, we used the surface concentration measurements. Using calculation equations, this study stresses the importance of the Saharan dust component of the total PM10 concentration measured at an industrial traffic monitoring station. The anthropogenic, re-suspension, and Saharan dust contributions were assessed for such events. The Saharan component is estimated to daily PM10 concentration reaching up to 88%. The anthropogenic component has been found to represent up to 12% during dust days. By comparison with the limit values, daily anthropogenic concentration remained high in Gabes station after subtracting the contribution of desert dust.

Health, Transport and the Environment: The Impacts of the COVID-19 Lockdown on Air Pollution.

Lockdown measures were initiated in Italy on March 9th after the start of the SARS-CoV-2 epidemic to flatten the epidemic curve. The aim of the present study was to assess the impact of restrictive measures in the Apulia Region, southern Italy, on air quality from March to April 2020. We applied a dual-track approach. We assessed citizen mobility and vehicle traffic with mobility network data and information obtained from satellite tracking, and we evaluated and compared pollutant concentration data as measured by monitoring stations maintained by the Regional Agency for Environmental Protection and Prevention of Apulia (ARPA). The results showed a decrease in the weekly mean NO2 concentration recorded by urban traffic stations during the lockdown period. In particular, in the city of Bari, the average NO2 concentration decreased from 62.2 μg/m3 in March 2019 to 48.2 μg/m3 in March 2020. Regarding PM10 levels, the average concentrations at the individual traffic stations showed no particular variation compared to those in the same months of the previous year, except for Bari-Caldarola Station in March 2019/2020 (p-value < 0.001) and in April 2019/2020 (p-value = 0.04). In particular the average in March 2019 was ~26.9 μg/m3, while that in March 2020 was ~22.9 μg/m3. For April, the average concentration of PM10 in 2019 was 27.9 μg/m3, while in 2020, the average was ~22.4 μg/m3. This can be explained by the fact that PM10 levels are influenced by multiple variables such as weather and climate conditions and desert dust advections.

Spatial-temporal prediction of ambient nitrogen dioxide and ozone levels over Italy using a Random Forest model for population exposure assessment

We developed an integrated approach coupling a chemical transport model (CTM) with machine learning (ML) techniques to produce high spatial resolution NO2 and O3 daily concentration fields over Italy. Three years (2013–2015) simulations, at a spatial resolution of 5 km, performed by the Flexible Air quality Regional Model (FARM) were used as predictors, together with other spatial-temporal data, such as population, land-use, surface greenness and road networks, by a ML Random Forest (ML-RF) algorithm to produce daily concentrations at higher resolution (1 km) over the national territory. The evaluation of the adopted integrated approach was based on NO2 and O3 observations available from 530 and 293 monitoring stations across Italy, respectively. A good performance for NO2 and excellent results for O3 were obtained from the application of the CTM; as for NO2, the levels at urban traffic stations were not captured by the simulations due to the adopted horizontal resolution and related emissions uncertainties. Performance improvements were achieved with ML-RF predictions, reducing NO2 underestimation (near zero fractional bias results) and better capturing spatial contrasts. The results obtained in this work were used to support the national exposure assessment and environmental epidemiology studies planned in the BEEP (Big data in Environmental and occupational Epidemiology) project and confirm the potential of machine learning methods to adequately predict air pollutant levels at high spatial and temporal resolutions.

Association of Air Pollution Levels to Atmospheric Weather Regimes over Europe

The ability to link air pollution to weather regimes may help municipalities to activate in advance plans to protect citizens from severe episodes of air pollution. The aim of this work was to associate the atmospheric circulation patterns and air pollution over Berlin using observational data from three air quality monitoring stations (Urban, Suburban and Traffic stations) during the period January 1990 to December 2002, with weather type’s classifications issued in the frame of the European Cooperation in Science and Technology project (COST733). Results obtained in this work shows similar higher ozone (O3) concentrations and weather regime type during the summer season for both the three stations and the two cluster methods used viz. Self-Organizing Maps (SOM) and cluster analysis of principal components (CAP). The highest concentration level of ozone was observed in the suburban station. High pollution that occurred from the particulate matter (PM10) was observed in the urban station during the winter season. The highest nitrogen dioxide (NO2) value was observed in the traffic station during the spring season. However, the traffic station is probably not a very good indicator of the sensitivity of air quality in meteorology, because it is too much affected by local sources.

Prediction of tropospheric ozone concentration using artificial neural networks at traffic and background urban locations in Novi Sad, Serbia.

In this paper, we described generation and performances of feedforward neural network models that could be used for a day ahead predictions of the daily maximum 1-h ozone concentration (1hO3) and 8-h average ozone concentration (8hO3) at one traffic and one background station in the urban area of Novi Sad, Serbia. The six meteorological variables for the day preceding the forecast and forecast day, ozone concentrations in the day preceding the forecast, the number of the day of the year, and the number of the weekday for which ozone prediction was performed were utilized as inputs. The three-layer perceptron neural network models with the best performance were chosen by testing with different numbers of neurons in the hidden layer and different activation functions. The mean bias error, mean absolute error, root mean squared error, correlation coefficient, and index of agreement or Willmott's Index for the validation data for 1hO3 forecasting were 0.005 μg m-3, 12.149 μg m-3, 15.926 μg m-3, 0.988, and 0.950, respectively, for the traffic station (Dnevnik), and - 0.565 μg m-3, 10.101 μg m-3, 12.962 μg m-3, 0.911, and 0.953, respectively, for the background station (Liman). For 8hO3 forecasting, statistical indicators were - 1.126 μg m-3, 10.614 μg m-3, 12.962 μg m-3, 0.910, and 0.948 respectively for the station Dnevnik and - 0.001 μg m-3, 8.574 μg m-3, 10.741 μg m-3, 0.936, and 0.966, respectively, for the station Liman. According to the Kolmogorov-Smirnov test, there is no significant difference between measured and predicted data. Models showed a good performance in forecasting days with the high values over a certain threshold.

Assessment of the Level of Knowledge of First Aid and Basic Life Support among the Police Workforce in a Coastal Area in Southern India

Background: First aid and basic life support (BLS) are the basic principles that have significant importance in the revival of victims on site. Although the epidemiology of injuries has been well studied in many developed countries, there are a fewer number of such similar studies among the police workforce in developing countries, such as India. Objective: The objective was to assess the level of understanding about first aid and BLS measures among the police workforce in a coastal city of Southern India. Materials and Methods: This descriptive cross-sectional study was conducted at police stations and traffic police stations in a coastal city of South India with a sample size of 196. A pretested semi-structured questionnaire assessed a plethora of parameters, such as choking and seizures. Data were analyzed using SPSS version 16.0 (IBM SPSS Inc Chicago, SPSS Inc) and results were obtained. Consent from the study participants was obtained before commencement. Results: About 87.3% of the study participants who received training markedly scored