The performance of a Decentralized Wastewater Treatment System (DWTS) comprising an Anaerobic Baffled Reactor (ABR) and an Anaerobic Filter (AF) and Membrane Filtration (MF) module was studied for domestic wastewater treatment. The efficiency of the system was evaluated by running ABR at four different HRTs (14, 12, 10, and 8 h) resulting in COD removal efficiencies of 74, 72, 69, and 65%, respectively. The performance of AF using four different filtration media, i.e., PVC pipe (25 mm), PVC pipe (20 mm), PVC pipe (15 mm), and Kaldnes K3, was determined at optimized HRT (12 h). Among all the filtration media tested, the highest performance efficiency of the system was found with the PVC pipe (20 mm), which showed COD, TP, and TKN removal of 79, 32, and 63%, respectively. The efficacy of the system was proven via significant COD and turbidity removal of 94.6 and 87.2%, respectively, by the combined system.
Precipitation plays a vital role in the economies of agricultural countries, such as Pakistan. Baluchistan is the largest province in Pakistan (in terms of land) and it is facing reoccurring droughts due to changing precipitation patterns. The landscape of the province consists of rugged terrain, mountains, hills, and valleys. The torrential rains lead to devastating flash floods due to the topography of the province, which has proven to be more catastrophic in nature. It is quite intriguing to observe the changing precipitation patterns in Baluchistan. Precipitation has become less frequent but intense, resulting in flash floods and landslides, as well as damage to agriculture, infrastructure, trade, environment, and the ecosystem. Baluchistan is under a drought warning and is already facing a water crisis. This study was performed on monthly precipitation time series data obtained from the Pakistan Meteorological Department (PMD) for determining trends in precipitation from 41 years of data (1977 to 2017) over 13 selected stations in Baluchistan. Due to the non-linear nature of the precipitation data, a non-parametric Mann–Kendall (MK) test was used to determine the increasing or decreasing trends in precipitation on a monthly basis. Large-scale atmospheric circulation and climate indices that affected precipitation were considered to determine their influence on precipitation. Statistical techniques of the partial Mann–Kendall (PMK) and Pearson correlation were applied to each station to ascertain the influence on precipitation due to climatic indices.
Exponentially rise of unstructured data is a question to all data scientists today. The graph of unstructured data is at such height that it consumes most of the storage of all clouds present today. Analysis through unstructured data is not easy due its high complexity and abstraction computations. There are different forms of unstructured data found inside web and different operations are done for their conversion. In this paper we tried to touch most of the unstructured data types and their related solution for specific conversion using data science analytics. Mongodb, famous for horizontal scaling is also used here for collection and storage of scalable and high volume data.
Remote sensing through satellites and internet of things (IoT) technology are two widespread techniques to assess inland water quality. However, both these techniques have their limitations. IoT provides point data, which is insufficient to represent entire water body, especially if the water body has complex terrain and hydrology. Through remote sensing, we can sample data of a large area, but data acquisition is constrained by satellite. Revisit time and quality of estimates can be affected by image resolution. Moreover, non-optical properties that might affect water quality cannot be sensed through satellites. To complement this, GIS data from labs can be useful for providing higher resolution and accurate data and can be used as ground truth. Thus, in this chapter, the authors aim to integrate both these data collection techniques followed by estimation and prediction through machine learning models. The accumulated datasets are used to train machine learning (ML) models deployed at a server. The selected ML model is an artificial neural network with train accuracy of 97% and test accuracy of 95%.
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