Morphological changes at Vellar estuary, India--impact of the December 2004 tsunami

Coastal subsystems formed by interaction of various processes, impacted by natural hazards like tsunami and storms, pose irreversible morphological changes. Vellar estuary, located on the southeast coast of India, with huge sand dunes (of 3-6m height and spread to 560ha) and barrier islands, has undergone extensive morphological changes due to the giant Indian Ocean tsunami that occurred on 26th December 2004. The damage caused by the tsunami has been quantified using extensive field data collected during pre- and post-tsunami periods through Real Time Kinematic GPS (for mapping coastal features and beach profiles) and Geographic Information System (GIS) couple. The tsunami with a wave height as high as 4m not only inundated the entire coastal land up to a maximum of 2km but also eroded the sand dunes and deposited the eroded material at the inlet, which ultimately formed as a vast tidal flat spread over 31ha. The estuary has suffered immensely due to the tsunami especially in terms of (i) loss of natural protection barriers (sand dunes), which made this coastal area more vulnerable to storm attack, and (ii) shallowness of inlet creating hindrance to navigation of fishing vessels. Based on the observations made at Vellar coast and past recovery experiences of tsunami/hurricanes elsewhere in the world, we contend that the morphological loss might take at least two annual cycles to regain its original form and the rebuilding of sand dunes may even take a decade.     

Correction to: Assessment of bioavailable nitrogen and phosphorus content in the sediments of Indian mangroves

Environmental Science and Pollution Research - A Correction to this paper has been published: https://doi.org/10.1007/s11356-021-14155-3     

Comparison of two estimation methods for surface area concentration using number concentration and mass concentration of combustion-related ultrafine particles

Recent research has suggested that the adverse health effects caused by nanoparticles are associated with their surface area (SA) concentrations. In this study, SA was estimated in two ways using number and mass concentrations and compared with SA (SA_meas) measured using a diffusion charger (DC). Aerosol measurements were made twice: once starting in October 2002 and again starting in December 2002 in Mysore, India in residences that used kerosene or liquefied petroleum gas (LPG) for cooking. Mass, number, and SA concentrations and size distributions by number were measured in each residence. The first estimation method (SA_PSD) used the size distribution by number to estimate SA. The second method (SA_INV) used a simple inversion scheme that incorporated number and mass concentrations while assuming a lognormal size distribution with a known geometrical standard deviation. SA_PSD was, on average, 2.4 times greater (range = 1.6–3.4) than SA_meas while SA_INV was, on average, 6.0 times greater (range = 4.6–7.7) than SA_meas. The logarithms of SA_PSD and SA_INV were found to be statistically significant predictors of the logarithm of SA_meas. The study showed that particle number and mass concentration measurements can be used to estimate SA with a correction factor that ranges between 2 and 6.     

Modeling Biodegradation of Nonylphenol

Nonylphenol is the primary breakdown product of nonylphenol ethoxylates, a certain class of nonionic surfactants. Nonylphenol has been found to be toxic to aquatic organisms and has been suspected of being harmful to humans due to its xenoestrogenic properties. Although there are known releases of nonylphenol to the environment, there is a lack of data describing the extent of biodegradation. This study thus focuses on much needed information on the biodegradation kinetics of nonylphenol. Oxygen uptake, cell growth and nonylphenol removal data were collected using batch reactors in an electrolytic respirometer. Nonylphenol removal, cell growth and substrate removal rates were modeled by the Monod, Haldane, Aiba, Webb, and Yano equations. The differential equations were solved by numerical integration to simulate cell growth, substrate removal, and oxygen uptake as a function of time. All models provided similar results with the Haldane model providing the best fit. The values of the kinetic parameters and the activation energy for nonylphenol were determined. These values can be used for predicting fate and transport of nonylphenol in the environment. The validity of applying each model to the biodegradation of nonylphenol was analyzed by computing the R ² values of each equation.     

Integrating spatial support tools into strategic planning—SEA of the GMS North–South Economic Corridor Strategy and Action Plan

The GMS countries, supported by the Asian Development Bank, have adopted a holistic, multidimensional approach to strengthen infrastructural linkages and facilitate cross border trade through (i) the establishment of a trans-boundary road connecting two economic nodes across marginalised areas, followed by 2) facilitation of environmentally and socially sound investments in these newly connected areas as a means to develop livelihoods. The North–South Economic Corridor is currently in its second phase of development, with investment opportunities to be laid out in the NSEC Strategy and Action Plan (SAP). It targets the ecologically and culturally sensitive border area between PR China's Yunnan Province, Northern Lao PDR, and Thailand. A trans-boundary, cross-sectoral Strategic Environmental Assessment was conducted to support the respective governments in assessing potential environmental and social impacts, developing alternatives and mitigation options, and feeding the findings back into the SAP writing process. Given the spatial dimension of corridor development—both with regard to opportunities and risks—particular emphasis was put in the application of spatial modelling tools to help geographically locate and quantify impacts as a means to guide interventions and set priorities.     

A 2019 Outbreak Investigation of Hepatitis A Virus Infections in the United States Linked to Imported Fresh Blackberries

Globally, hepatitis A virus (HAV) is one of the most common agents of acute viral hepatitis and causes approximately 1.4 million cases and 90,000 deaths annually despite the existence of an effective vaccine. In 2019, federal, state, and local partners investigated a multi-state outbreak of HAV infections linked to fresh blackberries sourced from multiple suppliers in Michoacán, Mexico. A total of 20 individuals with outbreak-related HAV infection were reported in seven states, including 11 hospitalizations, and no deaths. The Food and Drug Administration (FDA), the Centers for Disease Control and Prevention (CDC), and Nebraska State and Douglas County Health Departments conducted a traceback investigation for fresh blackberries reportedly purchased by 16 ill persons. These individuals reported purchasing fresh blackberries from 11 points of service from September 16 through 29, 2019 and their clinical isolates assessed through next-generation sequencing and phylogenetic analysis were genetically similar. The traceback investigation did not reveal convergence on a common grower or packing house within Mexico, but all of the blackberries were harvested from growers in Michoacán, Mexico. FDA did not detect the pathogen after analyzing fresh blackberry samples from four distributors, one consumer, and from nine importers at the port of entry as a result of increased screening. Challenges included gaps in traceability practices and the inability to recover the pathogen from sample testing, which prohibited investigators from determining the source of the implicated blackberries. This multi-state outbreak illustrated the importance of food safety practices for fresh produce that may contribute to foodborne illness outbreaks.

     

Annual and seasonal variability of ambient aerosols over an urban region in western India

Annual and seasonal variabilities in source contribution to total suspended particles (TSP) measured over an urban location in western India, Ahmedabad between May 2000 and January 2003 are examined in this study. Positive matrix factorization (PMF) resolved six factors including airborne regional dust, calcium carbonate rich dust, biomass burning/vehicular emissions, secondary nitrate/sulfate, marine aerosol, and smelter. In this study, non-parametric statistical tests including the Kruskal–Wallis analysis of variance (K–W ANOVA) and Spearman rank correlation (ρ) test were used to assess the annual and seasonal variations in factor contributions, and the influence of meteorology on these contributions, respectively. None of the factor contributions exhibited annual variations except airborne regional dust, and biomass burning/vehicular emissions factors. All of the factors exhibited seasonal variations. Several factor monsoon (July–September) median concentrations were significantly different from one or more of the other season medians. In general, it appeared that meteorological factors played a role in establishing the seasonal behavior of factor contributions. Factor contributions exhibited low to moderate correlations with meteorological parameters such as temperature, relative humidity, wind direction, and wind speed. Amongst all of the relationships, marine aerosol factor was reasonably well correlated with relative humidity (ρ = 0.73) and wind direction (ρ = 0.73) during the pre-monsoon season (March–May). This observation suggests that the aerosol transported by moisture laden winds from the Arabian sea contribute to this factor. The airborne regional dust factor was also moderately correlated with wind speed (ρ = 0.70) during the post-monsoon season. This relationship indicates that high regional dust concentrations are favored by high wind speeds and the resultant increase in dispersion.     

Application of recent advances in aerosol sampling science towards the development of improved sampling devices: the way ahead

This paper reviews the framework that underpins the development of a new generation of personal samplers capable of operating at much lower flowrates that those of the current generation and so capable of being used for exposure assessment not only for 'traditional' occupational populations (i.e., industrial workers) but also for people exposed to aerosols in the ambient atmosphere (including children). The opportunity for this new generation of samplers stems from the availability of very light and compact low-flowrate pumps. The development and deployment of such instruments presents: (a) physical challenges in terms of how to collect particle size fractions in a manner which is consistent with the new particle size-selective sampling criteria, and (b) analytical challenges in terms of how to quantitate the much smaller amounts of collected material that need to be analysed. The paper lays out the physical and analytical scenarios, and points the way forward to how such challenges can be overcome. Work is already in progress in several countries to develop prototype instruments for applications like those described.     

Cognitive performance during long-term residence in a polar environment

The objective of this study was to determine whether there was significant change in cognitive performance by prolonged duration of residence in polar environment. We examined volunteers (N = 23, scientific and logistic personnel) who spent fourteen months of continuous stay at ‘Maitri’ – The Indian research base in Antarctica (70 °45′S, 11 °44′E). Cognitive measures (tests of task acquisition, delayed recognition, attention and concentration, and digit symbol substitution) were obtained at the beginning (second month), the middle (seventh month) and the final (twelfth month) phases of prolonged residence in Antarctica. A repeated-measures MANOVA was conducted and the findings indicated a significant increase in percent accuracy in performance of task acquisition, delayed recognition and digit symbol tests that assessed recognition memory and learning. The test of attention and concentration that measured short-term memory for digits showed stable performance over a long-duration of residence in polar environment.     

Agricultural Irrigation Recommendation and Alert (AIRA) system using optimization and machine learning in Hadoop for sustainable agriculture

Environmental Science and Pollution Research - Internet of Things (IoT) in the field of agriculture promises to continuously provide global access to the farming information. The smart agriculture...