Assessment of heavy metal contamination within the sediments in some fresh water lakes of Udaipur

Karki, Dhanusha; Verma, Anuya

doi:10.6092/issn.2281-4485/13351

Cited by 2 publications

(2 citation statements)

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“…7a, the real-time temperature is reported (expressed in • C) as well as relative humidity (see related description in Appendix B); in Figure 7b, the PM 2.5 IAQ index versus the time (measurements of VOCs concentration) can be read. The adopted dust sensor (see Appendix B) counts the dust particles into a range between 0 to 3000 particles (PCS) per 0.01 contamination factor (CF) [19,20]. Specifically, the sensor measures the PM 2.5 IAQ index versus the time.…”

Section: Energy Monitoring and Iaq Dashboardsmentioning

confidence: 99%

“…Figure 7 represents the real-time serial output of only BME sensor (see Appendix B) evaluating the indoor air quality (IAQ) parameters (VOC concentration): in Figure7a, the real-time temperature is reported (expressed in • C) as well as relative humidity (see related description in Appendix B); in Figure7b, the PM 2.5 IAQ index versus the time (measurements of VOCs concentration) can be read. The adopted dust sensor (see Appendix B) counts the dust particles into a range between 0 to 3000 particles (PCS) per 0.01 contamination factor (CF)[19,20]. Specifically, the sensor measures the PM 2.5 IAQ index versus the time.…”

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confidence: 99%

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Advanced Data Systems for Energy Consumption Optimization and Air Quality Control in Smart Public Buildings Using a Versatile Open Source Approach

Starace¹,

Tiwari²,

Colangelo

et al. 2022

Electronics

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This work discusses smart building applications involving the Internet of Things (IoT) which are focused on energy consumption monitoring and forecasting systems, as well as indoor air quality (IAQ) control. Low-cost hardware integrating sensors and open source platforms are implemented for cloud data transmission, data storage and data processing. Advanced data analytics is performed by the seasonal autoregressive integrated moving average (SARIMA) method and a long short-term memory (LSTM) neural network with an accurate calculation performance about energy predictions. The proposed results are developed within the framework of the R&D project Data System Platform for Smart Communities (D-SySCOM), which is oriented to a smart public building application. The main goal of the work was to define a guideline-matching energy efficiency with wellness in public indoor environments, by providing modular low-cost solutions which are easily implementable for advanced data processing. The implemented technologies are suitable to define an efficient organizational user protocol based on energy efficiency and worker wellness. The estimated performance of mean square error (MSE) of 0.01 of the adopted algorithms proves the efficiency of the implemented building monitoring system in terms of energy consumption forecasting. In addition, the possibility of designing and implementing a modular low-cost hardware–software system was demonstrated utilizing open source tools in a way that was oriented to smart buildings approaches.

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Section: Energy Monitoring and Iaq Dashboardsmentioning

confidence: 99%

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confidence: 99%