RESUMEN

The spatial and temporal dynamics of daily ultraviolet index (UVI) for a period of 18 years (2004-2022) over the Indian state of Kerala were statistically characterised in the study. The UVI measurements used for the study were derived from the ultraviolet-B (UVB) irradiance measured by the Ozone Monitoring Instrument (OMI) of the AURA satellite and classified into different severity levels for analysis. Basic statistics of daily, monthly and seasonal UVI as well as Mann-Kendall (MK) statistical trend characteristics and the rate of change of daily UVI using Theil-Sen's slope test were also evaluated. A higher variability of UVI characteristics was observed in the Kerala region, and more than 79% of the measurements fell into the categories of very high and extreme UVI values, which suggests the need of implementation of appropriate measures to reduce health risks. Although the UVI measured during the study period shows a slight decrease, most of the data show a seasonal variation with undulating low and peak values. Higher UVI are observed during the months of March, April and September. The region also has higher UVI during the southwest monsoon (SWM) and summer seasons. Although Kerala region as a single whole unit, UVI show a non-significant decreasing trend (-0.83), the MK test revealed the increasing and decreasing trends of UVI ranging from -1.96 to 0.41 facilitated the delineation of areas (domains) where UVI are increasing or decreasing. The domain of UVI increase occupies the central and southern (S) parts, and the domains of decrease cover the northern (N) and S parts of the Kerala region. The rate of change of daily UVI in domain of increase and decrease shows an average rate of 0.34 × 10-5 day-1 and -2 × 10-5 day-1, respectively. The parameters (rainfall, air temperature, cloud optical depth (COD) and solar zenith angle (SZA)) that affect the strength of UV rays reaching the surface indicate that a cloud-free atmosphere or low thickness clouds prevails in the Kerala region. Overall, the study results indicate the need for regular monitoring of UVI in the study area and also suggest appropriate campaigns to disseminate information and precautions for prolonged UVI exposure to reduce the adverse health effects, since the study area has a high population density.

Asunto(s)

Ozono , Ozono/análisis , Tecnología de Sensores Remotos , Monitoreo del Ambiente , Rayos Ultravioleta , Estaciones del Año , India

RESUMEN

A method is developed that allows the construction of spatial emission inventories. The method is applied for anthropogenic SO2 over China (0.25°×0.25°). The Enhancement Ratio Method (ERM) allows for the calculation of SO2 emissions using relationships between gridded satellite measurements of SO2 and NO2 at low wind speeds, and satellite-based NOx emission estimates. Here, we derive SO2 emissions for five years (2007-2011). A large decrease of emissions during 2007-2009 and a modest increase between 2010 and 2011 is observed. The evolution of emissions over time calculated here is in general agreement with bottom-up inventories, although differences exist, not only between the current inventory and other inventories but also among the bottom up inventories themselves. The ERM-derived emissions are consistent, spatially and temporally, with existing inventories.

RESUMEN

South Africa has been measuring the ground-based solar UV index for more than two decades at six sites to raise awareness about the impacts of the solar UV index on human health. This paper is an exploratory study based on comparison with satellite UV index measurements from the OMI/AURA experiment. Relative UV index differences between ground-based and satellite-derived data ranged from 0 to 45% depending on the site and year. Most of time, these differences appear in winter. Some ground-based stations' data had closer agreement with satellite-derived data. While the ground-based instruments are not intended for long-term trend analysis, they provide UV index information for public awareness instead, with some weak signs suggesting such long-term trends may exist in the ground-based data. The annual cycle, altitude, and latitude effects clearly appear in the UV index data measured in South Africa. This variability must be taken into account for the development of an excess solar UV exposure prevention strategy.

Asunto(s)

Exposición a Riesgos Ambientales/análisis , Rayos Ultravioleta , Altitud , Humanos , Comunicaciones por Satélite , Estaciones del Año , Sudáfrica

RESUMEN

This study aims to construct and validate a neural network (NN) model for the production of high frequency (~1min) ground-based estimates of total ozone column (TOC) at a mid-latitude UV and ozone monitoring station in the Laboratory of Atmospheric Physics of the Aristotle University of Thessaloniki (LAP/AUTh) for the years 2005-2014. In the first stage of model development, ~30,000 records of coincident solar UV spectral irradiance measurements from a Norsk Institutt for Luftforskning (NILU)-UV multi-filter radiometer and TOC measurements from a co-located Brewer spectroradiometer are used to train a NN to learn the nonlinear functional relation between the irradiances and TOC. The model is then subjected to sensitivity analysis and validation. Close agreement is obtained (R2=0.94, RMSE=8.21 DU and bias=-0.15 DU relative to the Brewer) for the training data in the correlation of NN estimates on Brewer derived TOC with 95% of the coincident data differing by less than 13 DU. In the second stage of development, a long time series (≥1 million records) of high frequency (~1min) NILU-UV ground-based measurements are presented as inputs to the NN model to generate high frequency TOC estimates. The advantage of the NN model is that it is not site dependent and is applicable to any NILU input data lying within the range of the training data. GOME/ERS-2, SCIAMACHY/Envisat, OMI/Aura and GOME2/MetOp-A TOC records are then used to perform a precise cross-validation analysis and comparison with the NILU TOC estimates over Thessaloniki. All 4 satellite TOC dataset are retrieved using the GOME Direct Fitting algorithm, version 3 (GODFIT_v3), for reasons of consistency. The NILU TOC estimates within ±30min of the overpass times agree well with the satellite TOC retrievals with coefficient of determination in the range 0.88≤R2≤0.90 for all sky conditions and 0.95≤R2≤0.96 for clear sky conditions. The mean fractional differences are found to be -0.67%±2.15%, -1.44%±2.25%, -2.09%±2.06% and -0.85%±2.19% for GOME, SCIAMACHY, OMI and GOME2 respectively for the clear sky cases. The near constant standard deviation (~±2.2%) across the array of sensors testifies directly to the stability of both the GODFIT_v3 algorithm and the NN model for providing coherent and robust TOC records. Furthermore, the high Pearson product moment correlation coefficients (0.94