Abstract. The solar hourly global irradiation received at ground level estimated by the databases HelioClim3v4, HelioClim-3v5 and CAMS Radiation Service are compared to coincident measurements made in five stations in Oman and Abu Dhabi. CAMS is an abbreviation of Copernicus Atmosphere Monitoring Service. Each database describes the hour-to-hour changes in irradiation very well with correlation coefficients greater than 0.97 for all stations. Each database exhibits a tendency to underestimate the irradiation in this area; the bias is small and less than 5 % of the average of the measurements in absolute value. The RMSE ranges between 70 and 90 Wh m −2 (11 to 16 %). This validation of the three databases for this arid region on the edge of the Meteosat coverage reveals satisfactory results. Each database captures accurately the temporal and spatial variability of the irradiance field. It is found that the three databases do not exhibit noticeable geographical changes in performances and are reliable sources to assess the SSI in this region.

Abstract. HelioClim-3v4 (HC3v4), HelioClim-3v5 (HC3v5) and the radiation service version 2 of the Copernicus Atmosphere Monitoring Service (CAMS-Rad) are databases that contain hourly values of solar radiation at ground level. These estimated hourly irradiations are compared to coincident measurements made at five stations in Morocco. The correlation coefficients between measurements and estimates are similar for the three databases and around 0.97–0.98 for global irradiation. For the direct irradiation, the correlation coefficients are around 0.70–0.79 for HC3v4, 0.79–0.84 for HC3v5 and 0.78–0.87 for CAMS-Rad. For global irradiation, the bias relative to the average of the measurements is small and ranges between −6 and −1 % for HC3v4, −4 and 0 % for HC3v5, and −4 and 7 % for CAMS-Rad; HC3v4 and HC3v5 exhibit a tendency to slightly underestimate the global irradiation. The root mean square error (RMSE) ranges between 53 (12 %) and 72 Wh m−2 (13 %) for HC3v4, 55 (12 %) and 71 Wh m−2 (13 %) for HC3v5, and 59 (11 %) and 97 Wh m−2 (21 %) for CAMS-Rad. For the direct irradiation, the relative bias ranges between −16 and 21 % for HC3v4, −7 and 22 % for HC3v5, and −18 and 7 % for CAMS-Rad. The RMSE ranges between 170 (28 %) and 210 Wh m−2 (33 %) for HC3v4, 153 (25 %) and 209 Wh m−2 (40 %) for HC3v5, and 159 (26 %) and 244 Wh m−2 (39 %) for CAMS-Rad. HC3v5 captures the temporal and spatial variability of the irradiation field well. The performance is poorer for HC3v4 and CAMS-Rad which exhibit more variability from site to site. As a whole, the three databases are reliable sources on solar radiation in Morocco.

Several sectors need global horizontal irradiance (GHI) data for various purposes. However, the availability of a long-term time series of high quality in situ GHI measurements is limited. Therefore, several studies have tried to estimate GHI by re-analysing climate data or satellite images. Validation is essential for the later use of GHI data in the regions with a scarcity of ground-recorded data. This study contributes to previous studies that have been carried out in the past to validate HelioClim-3 version 5 (HC3v5) and the Copernicus Atmosphere Monitoring Service, using radiation service version 3 (CRSv3) data of hourly GHI from satellite-derived datasets (SDD) with nine ground stations in northeast Iraq, which have not been used previously. The validation is carried out with station data at the pixel locations and two other data points in the vicinity of each station, which is something that is rarely seen in the literature. The temporal and spatial trends of the ground data are well captured by the two SDDs. Correlation ranges from 0.94 to 0.97 in all-sky and clear-sky conditions in most cases, while for cloudy-sky conditions, it is between 0.51–0.72 and 0.82–0.89 for the clearness index. The bias is negative for most of the cases, except for three positive cases. It ranges from −7% to 4%, and −8% to 3% for the all-sky and clear-sky conditions, respectively. For cloudy-sky conditions, the bias is positive, and differs from one station to another, from 16% to 85%. The root mean square error (RMSE) ranges between 12–20% and 8–12% for all-sky and clear-sky conditions, respectively. In contrast, the RMSE range is significantly higher in cloudy-sky conditions: above 56%. The bias and RMSE for the clearness index are nearly the same as those for the GHI for all-sky conditions. The spatial variability of hourly GHI SDD differs only by 2%, depending on the station location compared to the data points around each station. The variability of two SDDs is quite similar to the ground data, based on the mean and standard deviation of hourly GHI in a month. Having station data at different timescales and the small number of stations with GHI records in the region are the main limitations of this analysis.

Environmental assessment of Municipal Solid Waste (MSW) management is essential. Life Cycle Assessment (LCA) is a powerful and widely used method, which implements causal chains (impact pathways) between the studied processes and their environmental impacts. However, in waste management, the method presents some weaknesses. For example, there is no impact category related to odour, whose assessment is nevertheless essential, especially when the organic fraction of waste is concerned. Odour interferes with human welfare and comfort. Sometimes, it can become a nuisance and be described as a "socio-environmental" impact. To integrate the impact of odour in waste management plans, it is necessary to build an odourimpact pathway. The aim of this paper is to present a first attempt to build such an impact pathway up to the so-called midpoint step (i.e. the level of discomfort to human beings). The methodology we developed is based on the cause/effect chain according to the descriptors of the Site Dependent approach. Unlike classical LCA, the classification step is more important and characterization is aimed at computing the characterization factor. The change in this classification step allows for working on the occurrence of odour impacts. To determine impact occurrence, it is necessary to integrate local conditions into odour assessment. This was done using (i) the USEtox model in which local conditions to assess odour impacts are integrated and (ii) the framework of a new methodology that takes into account background concentrations). The methodology was implemented in a case study, i.e. by computing atmospheric emission of ethyl benzene during composting (2.93.10-2 kg.d-1). The characterization factor for ethyl benzene was equal to 3.02.10-3 kg eq. Benzene per /kg emitted ethyl benzene. The daily emission of ethyl benzene generated an odour impact equal to 6.6.10-5 kg eq. benzene. With that first odour mid-point impact, we paved the way for the construction of a whole odour pathway (going up to end-point impacts or damages). However, several limits were identified such as data availability, the model under use and the use of average daily data which is less relevant than emission peaks. We should also recall that our methodology is not intended for predicting nuisance likely to disturb populations living nearby the facility. Its first objective is to provide an indicator that fits with LCA methodology in order to help local decision-makers to differentiate waste management scenarios based on exhaustive LCA.

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