Citizen science is increasingly being used in diverse research domains. With the emergence and rapid development of sensor technologies, citizens potentially have more powerful tools to collect data and generate information to understand their living environment. Although sensor technologies are developing fast, citizen sensing has not been widely implemented yet and published studies are only a few. In this paper, we analyse the practical experiences from an implementation of citizen sensing for urban environment monitoring. A bottom-up model in which citizens develop and use sensors for environmental monitoring is described and assessed. The paper focuses on a case study of Amsterdam Smart Citizens Lab using NO2sensors for air quality monitoring. We found that the bottom-up citizen sensing is still challenging but can be successful with open cooperation and effective use of online and offline facilities. Based on the assessment, suggestions are proposed for further implementations and research.

The wetness of plant leaf surfaces is an important parameter in the deposition process of atmospheric trace gases. Particularly gases with high water solubility tend to deposit faster to a wet surface, compared to a dry one. Further, drying up of a wet leaf surface may lead to revolatilization of previously deposited gases. Despite the high importance of leaf surface wetness in biosphere/atmosphere exchange, there is no quantitative description of this parameter on the ecosystem scale, quantifying its initiation, duration, dissipation, correlation with parameters such as air humidity, turbulence, vegetation type, plant physiology, and others. This contribution is a ®rst step towards a climatology of leaf surface wetness, based on a large data basis from various ecosystems. Leaf surface wetness was monitored at two grassland and two forest research sites in NW and central Europe throughout the vegetation period of 1998. It was sensed through measurement of the electrical conductivity between two electrodes that were clipped to the living plant leaf surfaces. This yields a relative signal that responds promptly to the presence of leaf wetness. A routine is presented that combines the data from several sensors to the dimensionless leaf wetness, LW, with values between zero and one. Periods of high leaf wetness (LW > 0.9) were in most cases triggered by precipitation events. After termination of rain, LW decreased quickly at the forest sites and dropped to values below 0.1 within less than 24 hours in most cases. At the grassland sites, the formation of dew led to a more complex pattern, with the occurrence of diurnal cycles of LW. Although periods of low relative air humidity (e.g., rH < 50%) are normally associated with periods of low leaf wetness, the extent of correlation between these two parameters at rH > 60% varies between the different sites. The grassland sites show very similar distributions of the LW data with rH, indicating a positive correlation between LW and rH, although there is much scatter in the relationship. One forest site also exhibited a positive correlation, although LW was typically lower for a given rH than at the grassland sites. At another forest site in central mountainous Europe, the correlation between LW and rH was less well established, with low leaf wetness (LW < 0.001) occurring within the entire air humidity range 60% < rH < 100%. We propose that leaf wetness should be included in routine measurement programs studying biosphere±atmosphere exchange.

This paper presents the utilization of surface fluxes and relative evapotranspiration derived from satellites for crop yield prediction using a dedicated crop growth simulation algorithm, the Environmental Analysis and Remote Sensing (EARS) Crop Growth Simulation algorithm (EARS-CGS). The objective was to test the EARS-CGS algorithm independent of ground data for crop yield prediction at national level in Europe. The algorithm is based on existing crop yield models but has been modified to assimilate satellite derived global solar radiation and actual evaporation information. The algorithm simulates crop biomass. A statistical method is utilized to relate crop biomass to crop yield and to correct for regional differences in yields that are not the result of radiation or water limitation. Six years of Meteosat data were processed to predict winter wheat and spring barley yields for Spain and the UK. The predicted yields were compared to the national reported yields and to forecasts of the European Statistical Office (EUROSTAT) and the Monitoring Agriculture by Remote Sensing-Crop Growth Monitoring System (MARS-CGMS). To evaluate the timeliness of the predictions the reported yields were compared to yield predictions made at different stages of the growing season. The results presented in this paper demonstrate that crop yields predicted from meteorological satellites can be applied to provide timely and reliable crop yield forecasts.

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Background: There is a need for non-invasive biomarkers to assess in vivo efficacy of protective measures aiming at reducing ultraviolet radiation (UVR) exposure. Stratum corneum (SC) biomarkers showed to be promising markers for internal UVR dose and immune response.Purpose: To establish a dose-response relationship for SC biomarkers and explore their suitability for in vivo assessment of the blocking effect of two sunscreens with a high sun protection factor (SPF) (50+).Methods: Twelve volunteers were exposed to a broad-spectrum UVB (280-320 nm), five times a week, during one week. Unprotected back skin was irradiated with 0.24, 0.48, 0.72 and 1.44 standard erythema dose (SED) and sunscreen-protected skin with 3.6 SED. SC samples for determination of the relative amount of cis-urocanic acid (cUCA) and thirteen immunological makers including cytokines and matrix metalloproteinases (MMP) were collected after each irradiation.Results: cUCA sharply increased after the first irradiation in a dose-dependent fashion. However, it levelled-off after subsequent exposures and reached a plateau for the highest UV-dose after the third irradiation. None of the immunological markers showed dose-dependency. However, MMP-9, IL-1β and CCL27 increased gradually from baseline during repetitive exposures to the highest UV-dose. Assessed from cUCA, both sunscreens blocked >98% of the applied UV-dose.Conclusions: cUCA is a sensitive, non-invasive marker of the internal UVR dose enabling in vivo assessment of the blocking effect of high SPF sunscreens in the UVB-region. Immunological SC markers show low sensitivity in detecting immune response at sub-erythemal UVR dosages, suggesting they might be suitable only at higher and/or repetitive UVR exposure.