Rapid urbanization has led to the exploitation of water quality and quantity. Urban growth and its activities result in the pollution of freshwater by generating different types of waste. Root Zone Technology (RZT) has successfully been adopted and employed in several countries to promote sustainable development. RZT paves the way for the incorporation of automated dynamics into an artificial soil ecosystem. This study’s primary goal was to develop a water treatment process for industrial effluents naturally and effectively using RZT. The technology adopts layers of coarse and fine aggregates, charcoal, sand, and planted filter beds consisting of compost media to treat effluents; the system is easily installed, low-maintenance, and has low operational costs. Selected plants achieved a result of 50–80% pollutant removal. RZT reduces the characteristics of effluents, such as chemical oxygen demand, biochemical oxygen demand, pH, color, TSS, TDS, BOD, COD, etc., by a more significant amount. Further studies of more plant species should be performed to improve this technology. Soil tests will also be an excellent option for understanding the concepts of reed absorption mechanisms. In addition, incorporating modeling in agricultural systems will be beneficial for future studies.
In this study, we observe the health effects experienced by the people living in that respective study area by analyzing the hospital admission data. A limited study on the association between air pollutants and the number of hospital admissions is available. The proposed research is an extended version of a previously published article, performed in the year 2019 during the color festival - "Holi", the colors used are widespread throughout the festival. Fine particles were monitored and their ion concentrations were analyzed by ion chromatograph. The significant anions (sulphate, nitrate, and chloride) and cations (sodium, potassium, and magnesium) were obtained in fine particles which were higher than the permissible limits. The collected data shows a 0.7% of the increase in hospital admissions after Holi. Dispersion modeling and trajectory analysis have been introduced to understand the dispersion of air pollutants during pre-holi, holi and post-holi. Thus, it is evident that the Holi festival potentially contributes to air pollution, which leads to serious health hazards.
This review was carried out to understand the retrieval of aerosol optical depth (AOD) datasets for estimating particle concentration and its influence on ambient air and surroundings by various models. Several studies have evaluated particle (PM 10 and PM 2.5 ) concentration profiles present in the lowest layers of the atmosphere by using AOD datasets. This study aimed at identifying consistent and precise particle estimation by various datasets retrieved by satellite‐based models for the ground‐level PM concentration. Extremities of satellite sensor data, like specific capabilities, as well as a few drawbacks are presented. Multi‐angle imaging spectroradiometer (MISR), visible infrared imaging radiometer suite (VIIRS) datasets, mixed‐effect model (MEM), and geographically weighted regression (GWR) models outperformed to estimate AOD and PM in comparison with the moderate resolution imaging spectroradiometer (MODIS) and other datasets. The improvised algorithms with higher resolution in the upcoming research would provide an even better estimation for AOD and PM.
Conservation and management are ponderous to monitor; the mangrove ecosystems are evident to be ecologically and economically significant to anthropomorphic populations. In spite of being viable and crucial biome services, mangroves frequently face threats from environmental disaster. The extraction for details to sustain and inspect the mangroves by adopting Gis and Hyperspectral data via Machine Learning. Previous research works have various dominant revelations in this particular domain and exhibit precision of 95% by this modus. The spectral profiles for 8 mangrove species were collected from various literatures. In the present study, Hyperion data of Pichavaram mangrove forest area is evaluated. The pre- processing of hyperspectral data is the prime emphasis, 242 bands were identified with bad pixels and fixed by replacing them with mean value of neighbouring pixels, where 179 bands are selected by MAD (Mean Median Absolute deviation) method. Endmember extraction was performed by deploying an hourglass workflow. MNF and PPI images were plotted in nD Visualiser and 48 endmembers were extracted. As they match the endmember with the ground truth data, distinct classifications are performed to map mangroves. By using SA classification, the abstraction of the mean spectra for definite mangrove pixels is obtained. Other unsupervised algorithms such as ISO clustering, K means Clustering are performed to map the study area. The findings of the study indicate the efficiency in pre-processing of hyperspectral data and absolute mappings of mangrove vegetations.
This chapter investigates the influence of Aerosol Optical Depth (AOD) on monsoon shifts and pollution in the Amaravathi River basin during the monsoon seasons of 2000, 2010, and 2022. The analysis reveals significant correlations between AOD levels and changes in monsoon, leading to alterations in rainfall patterns and seasonal temperatures. Higher AOD concentrations are linked to shifts in the monsoon season, impacting its onset, intensity, and duration, with implications for the region's ecosystems, agriculture, and water resources. Additionally, the chapter addresses pollution issues caused by urbanization, industrialization, and agriculture, contributing to water quality degradation and adversely affecting aquatic life. The study emphasizes the need to consider AOD levels when formulating climate resilience and pollution control strategies for the Amaravathi River basin, providing valuable insights for sustainable development and environmental management in the area.
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