Performance evaluation of grid-connected photovoltaic system for Kuttiady village in Kerala, India.

To combat the adverse environmental effects of fossil fuel burning for power generation and to conserve it for strategic use, new, clean, and renewable energy sources are being utilized for power generation. The study presents techno-economic analysis of a grid-connected solar photovoltaic (PV) power plant to partially meet the energy consumption of the people of Kuttiady village in Kerala, India. The proposed 2315.5 kW installed capacity PV is found to be feasible for the village and can produce 3878.3 MWh of energy annually while the demand is 4044.86 MWh at a plant capacity factor of 19.1% and cost of energy of 290.73 $/MWh. The performance of the proposed PV plant measured in terms of final yield (4.59 h), reference yield (5.64 h), and performance ratio (82%) is compatible and even higher with many such plants in India and other countries. Economic sensitivity analysis is also performed by varying the interest, discount, and inflation rates to check their effect on cost of energy, benefit cost ratio, and payback period. As the interest and discount rates decrease, the cost of energy and payback period also decreases while benefit cost ratio increases. The proposed plant can help in avoiding around 785 tons of greenhouse gases entering the local atmosphere of the Kuttiady village.

Nitrate transport in a fracture-skin-matrix system under non-isothermal conditions.

Subsurface leaching of agricultural runoff has been identified to pose a serious hazard to the soil-water ecosystem and human health, mostly due to the associated contamination with nitrate. Our understanding of the nature of contaminant spread in the vadose and aquifer zones has been improved from recent mechanistic models on the flow and transport of contaminants through fractured porous media. The present study aims to explore the impacts of skin formation in a fracture-matrix aquifer system onto the nitrogen species transport under non-isothermal settings using numerical modeling. A finite-difference scheme was employed to capture the nitrogen concentration profile and kinetics of transformation by solving the derived partial differential equations. The results show evidence of an additional mass transfer from fracture to skin so as to reduce the migration of nitrogen species (NO3-N and N2) at the fracture-matrix interface thereby reducing the peak concentration of N2 by nearly 1.5 times in fracture after denitrification. Although the thermal conductivity of the rock matrix has a direct impact on the temperature distribution in fracture-skin-matrix profiles, the presence of skin has a cooling effect for a high-temperature influent (45 °C), which also deteriorates the propagation of organic N2 and NO3-N, within the fracture. An increase in the temperature coefficient of skin has resulted in an apparent reduction in nitrogen species migration, indicating the thermo-chemical feasibility of an intermediate skin favoring the mass transfer processes. The findings of this study can be extended toward realistic estimation of groundwater contamination risks and for the design of biological filters for in situ remediation.

Evaluating sewage sludge contribution during co-composting using cause-evidence-impact analysis based on morphological characterization.

The pertinent challenges associated with effective treatment of fecal sludge in medium scales necessitate alternative means for land application. The methods of compost preparation from sewage sludge and their modes of application to the agricultural fields have profound impacts on the soil ecology and environment. Besides the chemical conditioning effects on soil organic matter, they also impart physical attributes to the soil texture and structure. Though it is expected that compost addition improves water holding capacity and nutrient sequestration, there is lack of clarity in correlating the field outcomes with conditions of excess nutrient storage/leaching despite the agronomic benefits. In this study, we present a systematic cause-evidence-impact relationship on the feedstock composition, processing, and applications of co-composted sewage sludge. Various analytical tools were compared to elucidate the unique characteristics of co-composted sewage sludge to get a realistic understanding of the complex soil-compost interactions. Results from the spectroscopic characterization reveal the implications of selection of bulking agents and sludge pre-treatment in determining the final quality of the compost. Based on the results, we postulate a unique attribution of parent material influence to the formation of well-defined porous structures which influences the nutrient leaching/sequestrating behavior of the soil. Thus, the compounded impacts of composted organic matter on the soil and crop can be proactively determined in terms of elemental composition, functional groups, and stability indices. The present approach provides good scope for customizing the preparations and applications of aerobic microbial composts in order to derive the preferred field outputs.

Wavelet kernel least square twin support vector regression for wind speed prediction.

Wind energy is a powerful yet freely available renewable energy. It is crucial to predict the wind speed (WS) accurately to make a precise prediction of wind power at wind power generating stations. Generally, the WS data is non-stationary and wavelets have the capacity to deal with such non-stationarity in datasets. While several machine learning models have been adopted for prediction of WS, the prediction capability of primal least square support vector regression (PLSTSVR) for the same has never been tested to the best of our knowledge. Therefore, in this work, wavelet kernel-based LSTSVR models are proposed for WS prediction, namely, Morlet wavelet kernel LSTSVR and Mexican hat wavelet kernel LSTSVR. Hourly WS data is gathered from four different stations, namely, Chennai, Madurai, Salem and Tirunelveli in Tamil Nadu, India. The proposed models' performance is assessed using root mean square, mean absolute, symmetric mean absolute percentage, mean absolute scaled error and R2. The proposed models' results are compared to those of twin support vector regression (TSVR), PLSTSVR and large-margin distribution machine-based regression (LDMR). The performance of the proposed models is superior to other models based on the results of the performance indicators.

Effects of air pollution on monumental buildings in India: An overview.

Recent advancements in environmental monitoring and analysis have created public and institutional awareness on the social and health impacts of air pollution at public places of tourists' attraction. Monuments stand as the celebrated remnants of bygone representations in the social and cultural tradition of any civilised state. India, being one of the oldest and live civilisations, owns numerous places of historical evidences in the form of both constructed museums and living monuments such as temples and palaces. Continuous exposure to the emission of gaseous and particulate pollutants has made remarkable evidences of damage to the artefacts and monumental structures located in major cities of the world. The aim of this study is to present an overview of the scientific attempts pertaining to the evaluation of impacts of air pollution and other meteorological changes on the historical monuments in India in the context of the global scenario. It is observed that seasonal fluctuations in the outdoor climate and increased human activities in the vicinity of the museums have plausible impacts on the immediate changes in the indoor air quality. The variations in the outdoor air quality are greatly affected by the traffic emissions and industrial emissions, while the indoor air quality is mostly affected by the improper ventilation and lack of proper control measures. The study provides strategies for developing air quality standards for museum environment and proposes a few technical and administrative solutions to improve the air quality for indoor museums as well as outdoor monuments.

Wind and wind power characteristics of the eastern and southern coastal and northern inland regions, South Africa.

The objective of this work is to understand the fluctuating nature of wind speed characteristics on different time scales and to find the long-term annual trends of wind speed at different locations in South Africa. The hourly average mean wind speed values over a period of 20 years are used to achieve the set objective. Wind speed frequency, directional availability of maximum mean wind speed, total energy, annual energy yield and plant capacity factors are determined for seven locations situated both inland and along the coast of South Africa. The highest mean wind speed (6.01 m/s) is obtained in Port Elizabeth and the lowest mean wind speed (3.86 m/s) is obtained in Bloemfontein. Wind speed increased with increasing latitudes at coastal sites (Cape Town, Durban, East London and Port Elizabeth), while the reverse trend was observed at inland locations (Bloemfontein, Johannesburg and Pretoria). Noticeable annual changes and relative wind speed values are found at coastal locations compared to inland sites. The energy pattern factor, also known as the cube factor, varied between a minimum of 1.489 in Pretoria and a maximum of 1.858 in Cape Town. Higher energy pattern factor (EPF) values correspond to sites with fair to good wind power potential. Finally, Cape Town, East London and Port Elizabeth are found to be good sites for wind power deployments based on the wind speed and power characteristics presented in this study.

Evaluation of suitability of wind speed probability distribution models: a case study from Tamil Nadu, India.

The optimal design and performance monitoring of wind farms depend on the precise assessment of spatial and temporal distribution of wind speed. The aim of this research is to investigate the appropriateness of nine popular probability distribution models (exponential, gamma, generalised extreme value, inverse Gaussian, Kumaraswamy, log-logistic, lognormal, Nakagami, and Weibull) for the assessment of wind speed distribution (WSD) at 10 sites situated at topographically distinct locations in Tamil Nadu, India, based on 39 years of data. The results suggest that a single distribution cannot produce best fit for all the stations. On an individual level, the generalised extreme value distribution provided the most suitable fit for majority of the stations, followed by the Kumaraswamy distribution. The Kumaraswamy distribution has performed well even if the WSD of the station is negatively skewed. Hence, based on the ranking and performance consistency, the Kumaraswamy distribution can be preferred irrespective of the topographical heterogeneity of the stations.

Correlating the trends of COVID-19 spread and air quality during lockdowns in Tier-I and Tier-II cities of India-lessons learnt and futuristic strategies.

The present study focuses on the impact of early imposed lockdowns and following unlocking phases on the status of air quality in six Tier-I and nine Tier-II cities of India as compared to the pre-lockdown measures. Furthermore, the study highlights the possible correlation of air quality index (AQI) with the initial trend of COVID-19 issues including the vaccination cases. Based on the statistical data analysis, we observed that the long-term averages for representing the short-term pre-lockdown conditions can impose a healing effect to the observed anomalies in air pollution data. However, the reduction in air pollution during the imposed lockdown series was only a phenomenal consequence, and the trends started reversing during the later phases of partial unlocking, where the correlation showed reversing trends. Being a yearly averaged parameter, the marginal reductions in the exceedance factor (EF) alone could not dictate air quality compared to the AQI. As there is incoherent variability in the pollutant distributions among the cities during various phases of the study, the trend analysis served as a preferable criterion to choose the preferred sources of variations. Based on the results, the correlation analysis revealed that air quality expressed in terms of AQI can act as an important precursor to estimate the critical phase of COVID-19 spread and the effectiveness of various control measures taken during each phase. Based on our proposed ranking, Kolkata and Patna are ranked first in the Tier-I and Tier-II cities respectively according to their responsiveness to the various institutionalized restrictions in terms of air quality parameters.

Short-term wind speed prediction using hybrid machine learning techniques.

Wind energy is one of the potential renewable energy sources being exploited around the globe today. Accurate prediction of wind speed is mandatory for precise estimation of wind power at a site. In this study, hybrid machine learning models have been deployed for short-term wind speed prediction. The twin support vector regression (TSVR), primal least squares twin support vector regression (PLSTSVR), iterative Lagrangian twin parametric insensitive support vector regression (ILTPISVR), extreme learning machine (ELM), random vector functional link (RVFL), and large-margin distribution machine-based regression (LDMR) models have been adopted in predicting the short-term wind speed collected from five stations named as Chennai, Coimbatore, Madurai, Salem, and Tirunelveli in Tamil Nadu, India. Further to check the applicability of the models, the performance of the models was compared based on various performance measures like RMSE, MAPE, SMAPE, MASE, SSE/SST, SSR/SST, and R2. The results suggest that LDMR outperforms other models in terms of its prediction accuracy and ELM is computationally faster compared to other models.

Confronting heterogeneous sorption and hydrodynamic dispersion on solute transport in a fracture-skin-matrix system using spatial moment analysis.

Understanding the intricacies of inter-dependency of fluid flow and solute transfer at the scale of a single fracture is limited by various simplifying assumptions employed for computational purposes. In the present study, the fracture-rock matrix interface is assumed to be consisting of a skin layer with sufficient mass transfer properties where non-linear adsorption is considered to be the limiting reaction among the various interfaces. A numerical model has been developed using implicit finite difference method with varying grids at the fracture-skin interface to capture the mass transfer during solute transport in the presence of non-linear Sips adsorption. The model was used to identify the critically influencing parameters on the temporal profiles of fluid velocity, macro-dispersion coefficient and dispersivity using the method of spatial moments. The results indicate that the presence of the skin has enhanced the mixing phenomenon as well as the sorptive mass transfer rates. Summary of the sensitivity analysis provides the critical factors to be considered while employing such comprehensive models for elucidating details at a small scale. The presence of fracture-skin evades the sensitive role played by (a) the fracture adsorption coefficient (with a reduced rate constant for adsorption and an increased rate constant for desorption) at early times, while resulting in an enhanced mixing characteristics at later times, and (b) maximum sorption capacity of fracture as a function of solute velocity; and in turn, it provides an improved control over the transportation of solutes through the fracture.

Influence of transient porosity in a coupled fracture-skin-matrix system at the scale of a single fracture.

The subsurface leaching of soluble chemicals in a fractured porous medium poses long-term risk of groundwater contamination. Tracing the occurrence, movement and consequences of such hydro-geo-chemical interactions is the fundamental process for an effective remediation plan. However, the complexity of geomorphology and mass transfer mechanisms makes it challenging while addressing these issues in a real field scale. The present study focuses on simulating the concentration profile of nitrate elution in a pseudo two-dimensional coupled fracture-skin-matrix system under active biodegradation using an implicit finite difference numerical technique. The interface between the fracture and rock matrix is assumed to possess a skin with time-varying porosity imitating the effect of bio-clogging. The results indicate that denitrification is significant in reducing the dissolved nitrate concentration for initial skin porosity of 10% in the presence of an unlimited oxygen and primary substrate. When the rate of change of skin porosity remains lower with a minimal variation, the nitrate concentration provided a considerable reduction in the vicinity of the fracture inlet. A similar trend is observed for dissolved oxygen concentration as well. The concentration profile of nitrate showed a higher rate of reduction with an increase in initial skin porosity value from smaller to significantly larger values. The present study clearly indicates the role of skin interface in depicting the solute concentration profile in fracture, especially during the washout of bio-clogged membrane (biofilm) attached to the rock matrix.