Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [presentation]

Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?

Objectives Wearable devices are currently at the heart of just about every discussion related to the Internet of Things. The requirement for self-health monitoring and preventive medicine is increasing due to the projected dramatic increase in the number of elderly people until 2020. Developed technologies are truly able to reduce the overall costs for prevention and monitoring. This is possible by constantly monitoring health indicators in various areas, and in particular, wearable devices are considered to carry this task out. These wearable devices and mobile apps now have been integrated with telemedicine and telehealth efficiently, to structure the medical Internet of Things. This paper reviews wearable health care devices both in scientific papers and commercial efforts. Methods MIoT is demonstrated through a defined architecture design, including hardware and software dealing with wearable devices, sensors, smart phones, medical application, and medical station analyzers for further diagnosis and data storage. Results Wearables, with the help of improved technology have been developed greatly and are considered reliable tools for long-term health monitoring systems. These are applied in the observation of a large variety of health monitoring indicators in the environment, vital signs, and fitness. Conclusions Wearable devices are now used for a wide range of healthcare observation. One of the most important elements essential in data collection is the sensor. During recent years with improvement in semiconductor technology, sensors have made investigation of a full range of parameters closer to realization.

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Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices.

Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?

The European Aerosol Research Lidar Network, EARLINET, was founded in 2000 as a research project for establishing a quantitative, comprehensive, and statistically significant database for the horizontal, vertical, and tempo- ral distribution of aerosols on a continental scale. Since then EARLINET has continued to provide the most extensive col- lection of ground-based data for the aerosol vertical distribu- tion over Europe. This paper gives an overview of the network's main de- velopments since 2000 and introduces the dedicated EAR- LINET special issue, which reports on the present innova- tive and comprehensive technical solutions and scientific re- sults related to the use of advanced lidar remote sensing tech- niques for the study of aerosol properties as developed within the network in the last 13 years. Since 2000, EARLINET has developed greatly in terms of number of stations and spatial distribution: from 17 sta- tions in 10 countries in 2000 to 27 stations in 16 countries in 2013. EARLINET has developed greatly also in terms of technological advances with the spread of advanced multi- wavelength Raman lidar stations in Europe. The develop- ments for the quality assurance strategy, the optimization of instruments and data processing, and the dissemination of data have contributed to a significant improvement of the net- work towards a more sustainable observing system, with an increase in the observing capability and a reduction of oper- ational costs. Consequently, EARLINET data have already been ex- tensively used for many climatological studies, long-range transport events, Saharan dust outbreaks, plumes from vol- canic eruptions, and for model evaluation and satellite data validation and integration. Future plans are aimed at continuous measurements and near-real-time data delivery in close cooperation with other ground-based networks, such as in the ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure Network) www.actris.net, and with the modeling and satellite commu- nity, linking the research community with the operational world, with the aim of establishing of the atmospheric part of the European component of the integrated global observ- ing system.

https://www.dwd.de/DWD/forschung/projekte/ceilomap/files/amt-7-2389-2014.pdf

EARLINET: towards an advanced sustainable European aerosol lidar network

The performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques are compared. A cluster of ozone, nitrogen dioxide, nitrogen monoxide, carbon monoxide and carbon dioxide sensors was operated. The sensors were either of metal oxide or electrochemical type or based on miniaturized infra-red cell. For each method, a two-week calibration was carried out at a semi-rural site against reference measurements. Subsequently, the accuracy of the predicted values was evaluated for about five months using a few indicators and techniques: orthogonal regression, target diagram, measurement uncertainty and drifts over time of sensor predictions. The study assessed if the sensors were could reach the Data Quality Objective (DQOs) of the European Air Quality Directive for indicative methods (between 25 and 30% of uncertainty for O 3 and NO 2 ). In this study it appears that O 3 may be calibrated using simple regression techniques while for NO 2 a better agreement between sensors and reference measurements was reached using supervised learning techniques. The hourly O 3 DQO was met while it was unlikely that NO 2 hourly one could be met. This was likely caused by the low NO 2 levels correlated with high O 3 levels that are typical of semi-rural site where the measurements of this study took place.

Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide

In this work the performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques, are compared. A cluster of either metal oxide or electrochemical sensors for nitrogen monoxide and carbon monoxide together with miniaturized infra-red carbon dioxide sensors was operated. Calibration was carried out during the two first weeks of evaluation against reference measurements. The accuracy of each regression method was evaluated on a five months field experiment at a semi-rural site using different indicators and techniques: orthogonal regression, target diagram, measurement uncertainty and drifts over time of sensor predictions. In addition to the analyses for ozone and nitrogen oxide already published in Part A [1], this work assessed if carbon monoxide sensors can reach the Data Quality Objective (DQOs) of 25% of uncertainty set in the European Air Quality Directive for indicative methods. As for ozone and nitrogen oxide, it was found for NO, CO and CO 2 that the best agreement between sensors and reference measurements was observed for supervised learning techniques compared to linear and multilinear regression.

Field calibration of a cluster of low-cost commercially available sensors for air quality monitoring. Part B: NO, CO and CO2

This experimental study focusses on air quality monitoring by low-cost and accurate sensors to provide a rank of air quality indices for citizens community in smart cities. Inter-comparison long-term measurements of CO, NO2, SO2 and PM10 are performed in a real scenario using referenced chemical analyzers at air-quality monitoring stations. The accuracy of the low-cost sensors is assessed in order to address the Data Quality Objectives (DQO) of the EU Air Quality Directive 2008/50/EC for indicative measurements. These preliminary results show that the low-cost sensors, when accurate, are suitable to define a rank of individual air quality index (AQI) to inform effectively general public and enhance environmental awareness. Finally, we show that the developed multiparametric sensor-system NASUS can be integrated both in a distributed city-network based on cost-effective fixed nodes, and in portable handheld sensor-systems to monitor air-pollution personal exposure.

Towards air quality indices in smart cities by calibrated low-cost sensors applied to networks

Using a clustered system of sensors able to measure O3, NO/NO2, CO and CO2, we want to compare the performance of several calibration methods. Based on the outcome of the single sensor's evaluation, the suitability of this cluster is investigated using deterministic or statistical methods such as multivariate regression and neural networks. The objective of the study is to assess if the measurement uncertainty of sensors can reach the Data Quality Objective of the European Air Quality Directive for indicative methods.

/pdf/calibration-of-a-cluster-of-low-cost-sensors-for-the-rh3iogzcep.pdf

Maria Gabriella Villani

Papers

Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide

Field calibration of a cluster of low-cost commercially available sensors for air quality monitoring. Part B: NO, CO and CO2

Towards air quality indices in smart cities by calibrated low-cost sensors applied to networks

Calibration of a cluster of low-cost sensors for the measurement of air pollution in ambient air

Transport of Volcanic Aerosol in the Troposphere: the Case Study of the 2002 Etna Plume