Constructing Local Sea Level Rise Scenarios for Assessing Possible Impacts and Adaptation Needs: Insights from Coasts of India

Rising seas are one of the crucial impacts of global warming. Rise in the mean sea level may impact coastal communities under an increasingly warming climate. The coastal zones are highly resourceful and dynamic. The coastal zones are facing many natural hazards such as erosion, storm surge, tsunami, coastal flooding and sea level rise. It is projected to have a three-time expansion of density of population in the coastal areas, and 50% of the world’s population will be occupied within the vicinity of 100 km of coastal areas. India has a very long coastline of 7500 km and covers 16.7% of the world’s population and has a very high population growth rate which itself make India highly sensitive to these environmental challenge. Projections of mean global sea level rise (GSLR) provide insufficient information to plan adaptive responses; local decisions require local projections that accommodate different risk tolerances and time frames and that can be linked to storm surge projections. Therefore, in this chapter, the main endeavor is to identify and compare coastal vulnerability to projected future sea level rise. In order to project the sea level rise at local level, a climate- and sea level rise simulator model output based on IPCC AR5 (Special Report on Emission Scenarios) has been employed under different scenarios. The results reveal that sea level for Visakhapatnam, Chennai, Cochin and Mumbai may increase by 1.16, 1.19, 1.34, 1.24 m, respectively, by 2100 under the high-emission business as usual carbon pollution scenario under IPCC AR5 Representative Concentration Pathway. The sea level of west coast tends to rise slightly more than the east coastal areas of India. These estimates have great potential for the coastal regulatory authority and other decision-makers to take precautions with regard to inundations of low-lying areas and to conserve India’s eco-sensitive coastal resources

Assessment of Population Exposure to Coarse and Fine Particulate Matter in the Urban Areas of Chennai, India

Research outcomes from the epidemiological studies have found that the course (PM 10 ) and the fine particulate matter (PM 2.5 ) are mainly responsible for various respiratory health effects for humans. The population-weighted exposure assessment is used as a vital decision-making tool to analyze the vulnerable areas where the population is exposed to critical concentrations of pollutants. Systemic sampling was carried out at strategic locations of Chennai to estimate the various concentration levels of particulate pollution during November 2013-January 2014. The concentration of the pollutants was classified based on the World Health Organization interim target (IT) guidelines. Using geospatial information systems the pollution and the high-resolution population data were interpolated to study the extent of the pollutants at the urban scale. The results show that approximately 28% of the population resides in vulnerable locations where the coarse particulate matter exceeds the prescribed standards. Alarmingly, the results of the analysis of fine particulates show that about 94% of the inhabitants live in critical areas where the concentration of the fine particulates exceeds the IT guidelines. Results based on human exposure analysis show the vulnerability is more towards the zones which are surrounded by prominent sources of pollution

Restoration of Degraded Soil in the Nanmangalam Reserve Forest with Native Tree Species: Effect of Indigenous Plant Growth-Promoting Bacteria

Restoration of a highly degraded forest, which had lost its natural capacity for regeneration, was attempted in the Nanmangalam Reserve Forest in Eastern Ghats of India. In field experiment, 12 native tree species were planted. The restoration included inoculation with a consortium of 5 native plant growth-promoting bacteria (PGPB), with the addition of small amounts of compost and a chemical fertilizer (NPK). The experimental fields were maintained for 1080 days. The growth and biomass varied depending on the plant species. All native plants responded well to the supplementation with the native PGPB. The plants such as Pongamia pinnata, Tamarindus indica, Gmelina arborea, Wrightia tinctoria, Syzygium cumini, Albizia lebbeck, Terminalia bellirica, and Azadirachta indica performed well in the native soil. This study demonstrated, by using native trees and PGPB, a possibility to restore the degraded forest

Pseudomonas aeruginosa RRALC3 Enhances the Biomass, Nutrient and Carbon Contents of Pongamia pinnata Seedlings in Degraded Forest Soil.

The study was aimed at assessing the effects of indigenous Plant Growth Promoting Bacterium (PGPB) on the legume Pongamia pinnata in the degraded soil of the Nanmangalam Reserve Forest (NRF) under nursery conditions. In total, 160 diazotrophs were isolated from three different nitrogen-free semi-solid media (LGI, Nfb, and JMV). Amongst these isolates, Pseudomonas aeruginosa RRALC3 exhibited the maximum ammonia production and hence was selected for further studies. RRALC3 was found to possess multiple plant growth promoting traits such as nitrogen accumulation (120.6ppm); it yielded a positive amplicon with nifH specific primers, tested positive for Indole Acetic Acid (IAA; 18.3μg/ml) and siderophore production, tested negative for HCN production and was observed to promote solubilization of phosphate, silicate and zinc in the plate assay. The 16S rDNA sequence of RRALC3 exhibited 99% sequence similarity to Pseudomonas aeruginosa JCM5962. Absence of virulence genes and non-hemolytic activity indicated that RRALC3 is unlikely to be a human pathogen. When the effects of RRALC3 on promotion of plant growth was tested in Pongamia pinnata, it was observed that in Pongamia seedlings treated with a combination of RRALC3 and chemical fertilizer, the dry matter increased by 30.75%. Nitrogen, phosphorus and potassium uptake increased by 34.1%, 27.08%, and 31.84%, respectively, when compared to control. Significant enhancement of total sugar, amino acids and organic acids content, by 23.4%, 29.39%, and 26.53% respectively, was seen in the root exudates of P. pinnata. The carbon content appreciated by 4-fold, when fertilized seedlings were treated with RRALC3. From the logistic equation, the rapid C accumulation time of Pongamia was computed as 43 days longer than the control when a combination of native PGPB and inorganic fertilizer was applied. The rapid accumulation time of N, P and K in Pongamia when treated with the same combination as above was 15, 40 and 33 days longer, respectively, as compared to the control

Data from: Critical analysis of forest degradation in the southern Eastern Ghats of India: comparison of satellite imagery and soil quality index

India has one of the largest assemblages of tropical biodiversity, with its unique floristic composition of endemic species. However, current forest cover assessment is performed via satellite-based forest surveys, which have many limitations. The present study, which was performed in the Eastern Ghats, analysed the satellite-based inventory provided by forest surveys and inferred from the results that this process no longer provides adequate information for quantifying forest degradation in an empirical manner. The study analysed 21 soil properties and generated a forest soil quality index of the Eastern Ghats, using principal component analysis. Using matrix modules and geospatial technology, we compared the forest degradation status calculated from satellite-based forest surveys with the degradation status calculated from the forest soil quality index. The Forest Survey of India classified about 1.8% of the Eastern Ghats’ total area as degraded forests and the remainder (98.2%) as open, dense, and very dense forests, whereas the soil quality index results found that about 42.4% of the total area is degraded, with the remainder (57.6%) being non-degraded. Our ground truth verification analyses indicate that the forest soil quality index along with the forest cover density data from the Forest Survey of India are ideal tools for evaluating forest degradation

Data from: Critical analysis of forest degradation in the southern Eastern Ghats of India: comparison of satellite imagery and soil quality index

India has one of the largest assemblages of tropical biodiversity, with its unique floristic composition of endemic species. However, current forest cover assessment is performed via satellite-based forest surveys, which have many limitations. The present study, which was performed in the Eastern Ghats, analysed the satellite-based inventory provided by forest surveys and inferred from the results that this process no longer provides adequate information for quantifying forest degradation in an empirical manner. The study analysed 21 soil properties and generated a forest soil quality index of the Eastern Ghats, using principal component analysis. Using matrix modules and geospatial technology, we compared the forest degradation status calculated from satellite-based forest surveys with the degradation status calculated from the forest soil quality index. The Forest Survey of India classified about 1.8% of the Eastern Ghats’ total area as degraded forests and the remainder (98.2%) as open, dense, and very dense forests, whereas the soil quality index results found that about 42.4% of the total area is degraded, with the remainder (57.6%) being non-degraded. Our ground truth verification analyses indicate that the forest soil quality index along with the forest cover density data from the Forest Survey of India are ideal tools for evaluating forest degradation

Assessment of Population Exposure to Coarse and Fine Particulate Matter in the Urban Areas of Chennai, India

Research outcomes from the epidemiological studies have found that the course (PM10) and the fine particulate matter (PM2.5) are mainly responsible for various respiratory health effects for humans. The population-weighted exposure assessment is used as a vital decision-making tool to analyze the vulnerable areas where the population is exposed to critical concentrations of pollutants. Systemic sampling was carried out at strategic locations of Chennai to estimate the various concentration levels of particulate pollution during November 2013–January 2014. The concentration of the pollutants was classified based on the World Health Organization interim target (IT) guidelines. Using geospatial information systems the pollution and the high-resolution population data were interpolated to study the extent of the pollutants at the urban scale. The results show that approximately 28% of the population resides in vulnerable locations where the coarse particulate matter exceeds the prescribed standards. Alarmingly, the results of the analysis of fine particulates show that about 94% of the inhabitants live in critical areas where the concentration of the fine particulates exceeds the IT guidelines. Results based on human exposure analysis show the vulnerability is more towards the zones which are surrounded by prominent sources of pollution

Root exudates componanents at 180 days after the emergence of <i>Pongamia pinnata</i> seedlings.

<p>¹ Values followed by different uppercase letters in a column are significantly different (<i>P</i> < 0.05) with in treatments.</p><p>T1, inorganic fertilizer (N:P:K, 2:1:1) + <i>P</i>. <i>aeruginosa</i> RRALC3; T2, inorganic fertilizer (N:P:K, 2:1:1) + commercial biofertilizer; T3, inorganic fertilizer (N:P:K, 2:1:1); T4, <i>P</i>. <i>aeruginosa</i> RRALC3; T5, control (no biofertilizer or inorganic fertilizer)</p><p>Root exudates componanents at 180 days after the emergence of <i>Pongamia pinnata</i> seedlings.</p

Effects of different treatments on the dry matter accumulation of <i>Pongamia pinnata</i> seedlings.

<p>¹ DMA, dry matter accumulation. Values represent the mean ± SD of three replicates. ² Values mentioned on the bar-chart are significantly different (<i>P</i> < 0.05) among treatments. ³ T1, inorganic fertilizer (N:P:K, 2:1:1) + <i>P</i>. <i>aeruginosa</i> RRALC3; T2, inorganic fertilizer (N:P:K, 2:1:1) + commercial biofertilizer; T3, inorganic fertilizer (N:P:K, 2:1:1); T4, <i>P</i>. <i>aeruginosa</i> RRALC3; T5, control (no biofertilizer or inorganic fertilizer).</p