Fourier transform infrared (FTIR) spectroscopy has been extensively used in microplastic (MP) pollution research since 2004. The aim of this review is to discuss and highlight the recent advances in FTIR (spectroscopy and chemical imaging) techniques that are used to characterize various polymer types of MPs and to trace their fate and transport in different environmental matrices. More than 400 research papers dealing with FTIR techniques in MP pollution research, which are published between January 2010 and December 2019, have been identified from the Scopus and Web of Science databases. The MPs present in sediment, water (marine and freshwater), biota, air/dust, waste water treatment plants and salt are further classified according to (1) characterization and identification, (2) weathering and aging, (3) ecotoxicology, and (4) analytical methods. The results revealed that the ATR-FTIR technique is mostly used to identify and characterize the MPs found in water and sediment. The mFTIR (FTIR imaging) is extensively used to study the ingestion of MPs in biota (both marine and freshwater). In this article, we have summarized the current knowledge of application of FTIR spectroscopy to MP research and provided insights to future challenges for understanding the risk of MPs.

Abstract. As a consequence of change in global climate, an increased frequency of natural hazards such as storm surges, tsunamis and cyclones, is predicted to have dramatic affects on the coastal communities and ecosystems by virtue of the devastation they cause during and after their occurrence. The tsunami of December 2004 and the Thane cyclone of 2011 caused extensive human and economic losses along the coastline of Puducherry and Tamil Nadu. The devastation caused by these events highlighted the need for vulnerability assessment to ensure better understanding of the elements causing different hazards and to consequently minimize the after-effects of the future events. This paper demonstrates an analytical hierarchical process (AHP)-based approach to coastal vulnerability studies as an improvement to the existing methodologies for vulnerability assessment. The paper also encourages the inclusion of socio-economic parameters along with the physical parameters to calculate the coastal vulnerability index using AHP-derived weights. Seven physical-geological parameters (slope, geomorphology, elevation, shoreline change, sea level rise, significant wave height and tidal range) and four socio-economic factors (population, land use/land cover (LU/LC), roads and location of tourist areas) are considered to measure the physical vulnerability index (PVI) as well as the socio-economic vulnerability index (SVI) of the Puducherry coast. Based on the weights and scores derived using AHP, vulnerability maps are prepared to demarcate areas with very low, medium and high vulnerability. A combination of PVI and SVI values are further utilized to compute the coastal vulnerability index (CVI). Finally, the various coastal segments are grouped into the 3 vulnerability classes to obtain the coastal vulnerability map. The entire coastal extent between Muthiapet and Kirumampakkam as well as the northern part of Kalapet is designated as the high vulnerability zone, which constitutes 50 % of the coastline. The region between the southern coastal extent of Kalapet and Lawspet is the medium vulnerability zone and the remaining 25 % is the low vulnerability zone. The results obtained enable the identification and prioritization of the more vulnerable areas of the region in order to further assist the government and the residing coastal communities in better coastal management and conservation.

Abstract. Sea level in the Singapore Strait (SS) exhibits response to various scale phenomena, from local to global. Longest tide gauge records in SS are analysed to derive local sea level trend and annual, inter-annual and multi-decadal sea level variability, which then are attributed to regional and global phenomena. Annual data gaps are reconstructed using functions correlating sea level variability with ENSO. At annual scale, sea level anomalies in SS are (quasi-periodic) monsoon-driven, of the order of ± 20 cm, the highest during northeast monsoon and the lowest during southwest monsoon. Interannual regional sea level drops are associated with El Niño events, while the rises are correlated with La Niña episodes; both variations are in the range of ± 5 cm. At multidecadal scale, annual measured sea levels in SS are varying with global mean sea level, rising at the rate 1.2-1.7 mm yr −1 for 1975-2009, 1.8-2.3 mm yr −1 for 1984-2009 and 1.9-4.6 mm yr −1 for 1993-2009. When SS rates are compared with the global trends (2.0, 2.4 and 2.8 mm yr −1 , respectively) derived from tide gauge measurements for the same periods, they are smaller in the earlier era and considerably larger in the recent one. Taking into account the first estimate of land subsidence rate, 1-1.5 mm yr −1 in Singapore, the recent trend of absolute sea level rise in SS follows regional tendency.

XBT fall-rate variation in waters of extreme temperature and the resulting depth error has been addressed using controlled XBT–CTD datasets collected from two cruises in the Southern Ocean. Mean depth errors deduced from both the datasets are significantly different from those reported earlier for tropical and subtropical regions. The comprehensive study of Hanawa et al. (making use of controlled XBT–CTD data, mostly from tropical and subtropical waters) showed that the manufacturer's equation underestimates the probe's fall rate. This is manifested by the mean negative depth error reported from this region. However, results from the present study show that the manufacturer's equation slightly overestimates the fall rate in this region, as indicated by the small positive error (5–10 m). In order to provide theoretical support to the observed depth error, an analytical approach is adopted based on the viscosity effect on the probe's fall rate. Observed as well as analytical results suggest that the probe has a decelerating tendency due to the viscosity effect in high-latitude waters, and the existing correction scheme is not appropriate for XBT data from regions of such extreme low temperature. The existing correction scheme is valid for tropical and subtropical waters of negative depth error zones. However, for XBT data from high-latitude waters it is reasonable not to correct XBT data based on the existing scheme until the exact nature of depth error from this region is known. Though the mean depth errors from both the datasets show nearly identical values, it is necessary to conduct more controlled XBT–CTD experiments in this region in order to substantiate the exact nature of error for this region and then develop an appropriate depth-correction scheme.