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We introduce the Precipitation Probability DISTribution (PPDIST) dataset, a collection of global high-resolution (0.1°) observation-based climatologies (1979-2018) of the occurrence and peak intensity of precipitation (P) at daily and 3-hourly time-scales. The climatologies were produced using neural networks trained with daily P observations from 93,138 gauges and hourly P observations (resampled to 3-hourly) from 11,881 gauges worldwide. Mean validation coefficient of determination (R2) values ranged from 0.76 to 0.80 for the daily P occurrence indices, and from 0.44 to 0.84 for the daily peak P intensity indices. The neural networks performed significantly better than current state-of-the-art reanalysis (ERA5) and satellite (IMERG) products for all P indices. Using a 0.1 mm 3 h-1 threshold, P was estimated to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively. The highest P intensities were found over parts of Central America, India, and Southeast Asia, along the western equatorial coast of Africa, and in the intertropical convergence zone. The PPDIST dataset is available via www.gloh2o.org/ppdist .
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BACKGROUND: Management and workers in small and medium-sized enterprises (SMEs) often find it hard to comprehend the requirements related to controlling risks due to exposure to substances. An intervention study was set up in order to support 45 SMEs in improving the management of the risks of occupational exposure to chemicals, and in using the control banding tool and exposure model Stoffenmanager in this process. METHODS: A 2-year intervention study was carried out, in which a mix of individual and collective training and support was offered, and baseline and effect measurements were carried out by means of structured interviews, in order to measure progress made. A seven-phase implementation evolutionary ladder was used for this purpose. Success and failure factors were identified by means of company visits and structured interviews. RESULTS: Most companies clearly moved upwards on the implementation evolutionary ladder; 76% of the companies by at least one phase, and 62% by at least two phases. Success and failure factors were described. CONCLUSION: Active training and coaching helped the participating companies to improve their chemical risk management, and to avoid making mistakes when using and applying Stoffenmanager. The use of validated tools embedded in a community platform appears to support companies to organize and structure their chemical risk management in a business-wise manner, but much depends upon motivated occupational health and safety (OHS) professionals, management support, and willingness to invest time and means.
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Many control banding tools use hazard banding in risk assessments for the occupational handling of hazardous substances. The outcome of these assessments can be combined with advice for the required risk management measures (RMMs). The Globally Harmonised System of Classification and Labelling of Chemicals (GHS) has resulted in a change in the hazard communication elements, i.e. Hazard (H) statements instead of Risk-phrases. Hazard banding schemes that depend on the old form of safety information have to be adapted to the new rules. The purpose of this publication is to outline the rationales for the assignment of hazard bands to H statements under the GHS. Based on this, this publication proposes a hazard banding scheme that uses the information from the safety data sheets as the basis for assignment. The assignment of hazard bands tiered according to the severity of the underlying hazards supports the important principle of substitution. Additionally, the set of assignment rules permits an exposure-route-specific assignment of hazard bands, which is necessary for the proposed route-specific RMMs. Ideally, all control banding tools should apply the same assignment rules. This GHS-compliant hazard banding scheme can hopefully help to establish a unified hazard banding strategy in the various control banding tools.
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Sustancias Peligrosas/efectos adversos , Sustancias Peligrosas/clasificación , Exposición Profesional/efectos adversos , Humanos , Salud Laboral , Etiquetado de Productos/métodos , Medición de Riesgo/métodos , Gestión de Riesgos/métodos , SeguridadRESUMEN
Stoffenmanager Nano (version 1.0) is a risk-banding tool developed for employers and employees to prioritize health risks occurring as a result of exposure to manufactured nano objects (MNOs) for a broad range of worker scenarios and to assist implementation of control measures to reduce exposure levels. In order to prioritize the health risks, the Stoffenmanager Nano combines the available hazard information of a substance with a qualitative estimate of potential for inhalation exposure. The development of the Stoffenmanager Nano started with a review of the available literature on control banding. Input parameters for the hazard assessment of MNOs were selected based on the availability of these parameters in, for instance, Safety Data Sheets or product information sheets. The conceptual exposure model described by Schneider et al. (2011) was used as the starting point for exposure banding. During the development of the Stoffenmanager Nano tool, the precautionary principle was applied to deal with the uncertainty regarding hazard and exposure assessment of MNOs. Subsequently, the model was converted into an online tool (http://nano.stoffenmanager.nl), tested, and reviewed by a number of companies. In this paper, we describe the Stoffenmanager Nano. This tool offers a practical approach for risk prioritization in exposure situations where quantitative risk assessment is currently not possible. Updates of this first version are anticipated as more data become available in the future.
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Contaminantes Ocupacionales del Aire/clasificación , Industrias/normas , Exposición por Inhalación/prevención & control , Nanoestructuras/clasificación , Exposición Profesional/prevención & control , Gestión de Riesgos/métodos , Contaminantes Ocupacionales del Aire/normas , Contaminantes Ocupacionales del Aire/toxicidad , Algoritmos , Humanos , Exposición por Inhalación/efectos adversos , Exposición por Inhalación/estadística & datos numéricos , Internet , Modelos Biológicos , Nanoestructuras/toxicidad , Exposición Profesional/efectos adversos , Exposición Profesional/estadística & datos numéricos , Tamaño de la Partícula , Dispositivos de Protección Respiratoria , Medición de Riesgo/métodos , Programas InformáticosRESUMEN
OBJECTIVES: There is a growing awareness of the potential risks for human health of exposure to ultrafine particles or nanoparticles. In that context, workplace air measurements become important, and various strategies have been developed to monitor exposure. In addition, observations and time/activity registrations are part of the exposure assessment strategy in many studies. Video exposure monitoring (VEM) can be of added value in these strategies. VEM combines exposure data with simultaneous video pictures of the process. METHODS: The PIMEX method (Picture Mix Exposure) was used as the VEM studied. The possibility to combine PIMEX and measurement instruments for nanoparticles was the object of this study. The starting point was a review of available instruments for workplace air measurements of nanoparticles. Publications of strategies to assess exposure to nanoparticles were also studied to review whether observations were part of these strategies. Finally, a technical review of combining PIMEX and the compatible measurement instruments was undertaken and explored as part of the strategy to assess exposure to nanoparticles. RESULTS: A variety of instruments are used to measure nanoparticles. One category is (near) real-time monitoring instruments, which determine numbers and particle size distribution or surface area concentration. Other instruments require sample collection in order to characterize the nanoparticles chemically and physically by microscopic analyses and/or elemental analyses. Only some of these instruments are technically compatible with PIMEX. With the PIMEX2008 version 1.02 software, it is possible to synchronize up to four different measuring instruments simultaneously with the video recording. CONCLUSIONS: PIMEX as a VEM method can be a useful tool as part of the strategy to assess exposure to nanoparticles. It can also be of value for other purposes like training, education, and risk communication. The possibility to synchronize more than one measuring instrument can be useful to simultaneously monitor different targets in the workplace, e.g. worker exposure in the breathing zone and background concentration.