Packiam Saranya

Bio: Packiam Saranya is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Ensemble forecasting & Tropospheric ozone. The author has an hindex of 2, co-authored 2 publications receiving 13 citations.

A novel bagging ensemble approach for predicting summertime ground-level ozone concentration.

Abstract: Ozone pollution appears as a major air quality issue, e.g. for the protection of human health and vegetation. Formation of ground level ozone is a complex photochemical phenomenon and involves numerous intricate factors most of which are interrelated with each other. Machine learning techniques can be adopted to predict the ground level ozone. The main objective of the present study is to develop the state-of-the-art ensemble bagging approach to model the summer time ground level ozone in an industrial area comprising a hazardous waste management facility. In this study, the feasibility of using ensemble model with seven meteorological parameters as input variables to predict the surface level O3 concentration. Multilayer perceptron, RTree, REPTree, and Random forest were employed as the base learners. The error measures used for checking the performance of each model includes IoAd, R2, and PEP. The model results were validated against an independent test data set. Bagged random forest predicted the ground level ozone better with higher Nash-Sutcliffe coefficient 0.93. This study scaffolded the current research gap in big data analysis identified with air pollutant prediction. Implications: The main focus of this paper is to model the summer time ground level O3 concentration in an Industrial area comprising of hazardous waste management facility. Comparison study was made between the base classifiers and the ensemble classifiers. Most of the conventional models can well predict the average concentrations. In this case the peak concentrations are of importance as it has serious effect on human health and environment. The models developed should also be homoscedastic.

Assessment of tropospheric ozone at an industrial site of Chennai megacity

Abstract: This paper presents the temporal variation in surface-level ozone (O3) measured at Gummidipoondi near Chennai, Tamilnadu. The site chosen for the present study has high potential for ozone ...

Photocatalytic mineralization of hydrogen sulfide as a dual-phase technique for hydrogen production and environmental remediation

Abstract: Hydrogen sulfide (H2S) is regarded as a broad-spectrum poison associated with severe health consequences. Among the available treatment options, photocatalytic technology may be effectively applied to the production of hydrogen gas through the splitting of H2S molecules and the addition of 79.9 kJ mol−1 of energy. As a result, advanced photo-reactive media may provide a win-win strategy to treat the parent pollutant (H2S) while producing hydrogen gas. This review encompasses both TiO2 and non-TiO2 catalysts capable of operating under ultraviolet, visible, and solar light irradiation. The performances of photocatalysts are assessed in terms of quantum yield, space-time yield, and other operational variables, including mode of operation, irradiation time, and relative humidity. The concept of space velocity is used to compare photocatalysts in reference to benchmark parameters for the treatment of H2S. This review addresses current limitations and future prospects of the application of photocatalytic technology to efficiently mitigate H2S pollution.

Soft Computing Applications in Air Quality Modeling: Past, Present, and Future

Abstract: Air quality models simulate the atmospheric environment systems and provide increased domain knowledge and reliable forecasting. They provide early warnings to the population and reduce the number of measuring stations. Due to the complexity and non-linear behavior associated with air quality data, soft computing models became popular in air quality modeling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modeling approaches. Among the many soft computing techniques in AQM, this article reviews and discusses artificial neural network (ANN), support vector machine (SVM), evolutionary ANN and SVM, the fuzzy logic model, neuro-fuzzy systems, the deep learning model, ensemble, and other hybrid models. Besides, it sheds light on employed input variables, data processing approaches, and targeted objective functions during modeling. It was observed that many advanced, reliable, and self-organized soft computing models like functional network, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning are rarely explored in AQM. Therefore, the partially explored and unexplored soft computing techniques can be appropriate choices for research in the field of air quality modeling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modeling context.

Characteristics of Ozone Pollution, Regional Distribution and Causes during 2014–2018 in Shandong Province, East China

Abstract: The summer ozone pollution of Shandong province has become a severe problem in the period 2014–2018. Affected by the monsoon climate, the monthly average ozone concentrations in most areas were unimodal, with peaks in June, whereas in coastal areas the concentrations were bimodal, with the highest peak in May and the second highest peak in September. Using the empirical orthogonal function method, three main spatial distribution patterns were found. The most important pattern proved the influences of solar radiation, temperature, and industrial structure on ozone. Spatial clustering analysis of the ozone concentration showed Shandong divided into five units, including Peninsula Coastal area (PC), Lunan inland area (LN), Western Bohai area (WB), Luxi plain area (LX), and Luzhong mountain area (LZ). Influenced by air temperature and local circulation, coastal cities had lower daytime and higher nighttime ozone concentrations than inland. Correlation analysis suggested that ozone concentrations were significantly positively correlated with solar radiation. The VOCs from industries or other sources (e.g., traffic emission, petroleum processing, and chemical industries) had high positive correlations with ozone concentrations, whereas NOx emissions had significantly negatively correlation. This study provides a comprehensive understanding of ozone pollution and theoretical reference for regional management of ozone pollution in Shandong province.

Physiological significance of pedospheric nitric oxide for root growth, development and organismic interactions.

Abstract: Nitric oxide (NO) is essential for plant growth and development, as well as interactions with abiotic and biotic environments. Its importance for multiple functions in plants means that tight regulation of NO concentrations is required. This is of particular significance in roots, where NO signalling is involved in processes, such as root growth, lateral root formation, nutrient acquisition, heavy metal homeostasis, symbiotic nitrogen fixation and root-mycorrhizal fungi interactions. The NO signal can also be produced in high levels by microbial processes in the rhizosphere, further impacting root processes. To explore these interesting interactions, in the present review, we firstly summarize current knowledge of physiological processes of NO production and consumption in roots and, thereafter, of processes involved in NO homeostasis in root cells with particular emphasis on root growth, development, nutrient acquisition, environmental stresses and organismic interactions.